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A Fuzzy-Optimization Approach for Generation

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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 4, NOVEMBER 20071665A Fuzzy-Optimization Approach for Generation Scheduling With Wind and Solar Energy SystemsRuey-Hsun Liang, Member, IEEE, and Jian-Hao LiaoAbstract—This paper presents a fuzzy-optimization approach for solving the generation scheduling problem with consideration of wind and solar energy systems. Wind and solar energy are being considered in the power system to schedule unit power output to minimize the total thermal unit fuel cost. When performing the generation scheduling problem in conventional methods, the hourly load, available water, wind speed, solar radiation must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy-optimization approach is that the forecast hourly load, available water, wind speed and solar radiation errors can be taken into account using fuzzy sets. Fuzzy set notations in the hourly load, available water, wind speed, solar radiation, spinning reserve and total fuel cost are developed to obtain the optimal generation schedule under an uncertain environment. To demonstrate the effectiveness of the proposed method, the generation scheduling problem is performed in a simplified generation system. The results show that a proper generating schedule for each unit can be reached using the proposed method. Index Terms—Fuzzy-optimization approach, fuzzy sets, generation scheduling, renewable energy.I. INTRODUCTIONRENEWABLE energy, such as solar energy and wind energy, has been actively researched and developed in advanced countries. This paper considers generating renewable energy connected to a power system to minimize the total thermal-unit fuel cost. It is a typical optimization problem in which the total operating cost over the study period is minimized subject to the load and all system constraints. A survey of the literature on this problem reveals that various numerical optimization techniques [1]–[11] have been employed to solve the generation scheduling problem. Quite promising results in terms of fuel cost savings have been reached in most works. However, among these works, some uncertain factors were not involved in the generation scheduling problem and renewable energy systems were not considered. A fuzzy-optimization approach is used in this paper to solve the generation scheduling problem considering wind and solar energy systems. To perform the generation scheduling problemManuscript received June 29, 2006; revised March 17, 2007. This work was supported by the National Science Council of Taiwan, R.O.C., through Project NSC94-2213-E-224-043. Paper no. TPWRS-00403-2006. The authors are with the Department of Electrical Engineering, National Yunlin University of Science and Technology, 640 Yunlin, Taiwan (e-mail: liangrh@.tw; g9312728@.tw). Color versions of one or more of the figures in this paper are available online at . Digital Object Identifier 10.1109/TPWRS.2007.907527using conventional methods, the hourly load for the power balance equations, available water for hydro units, wind speed for wind power generation, and solar radiation for photovoltaic (PV) generators must be forecasted to prevent errors. However, there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy-optimization approach is that the errors in the forecast hourly load, available water, wind speed, and solar radiation can be taken into account using fuzzy sets. An approach based on fuzzy sets is proposed to reach the desired generation schedules based on uncertain load demand, available water, wind speed and solar radiation. This method has been successfully applied to other optimization problems such as integrated power system scheduling [12], dynamic economic dispatch [13], and so on. To reach an optimal generation schedule under a fuzzy environment, the total fuel cost, load demand, spinning reserve, available water, wind speed, and solar radiation are all expressed in fuzzy set notations. A genetic algorithm [14], [15] is then employed to reach the desired generation schedule. , , and emission constraint will be conThe sidered in the generation scheduling problem for environmental protection. Power production from fossil burning and energy use may bring about significant adverse environmental effects , , emissions. This emission constraint through will be considered in the generation scheduling problem. In general, these emissions can be expressed as a nonlinear function of power generation. Based on the air emission data from the Taiwan power system thermal units, the pollution models are built using pollution models in the generation scheduling problem. The fuzzy-optimization approach is proposed to solve the generation scheduling problem considering wind and solar energy systems. The effectiveness and application of the proposed approach are demonstrated using a simplified generation system.II. PROBLEM FORMULATION The generation system under study is shown in Fig. 1. It is comprised of thermal system, hydro system, wind and solar energy systems. The problem model does not consider system configuration and line impedances. The objective of the short-term generation scheduling problem considering wind and solar energy systems is to determine the optimal amounts of generation power for the hydro units, thermal units, wind, and solar energy systems over the study period so that the total thermal unit fuel cost is minimized subject to power balance equations, spinning reserve requirements and other constraints. To do this, the study0885-8950/$25.00 © 2007 IEEE1666IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 4, NOVEMBER 2007power output of solar energy system at hour ; forecast system load demand at hour . b) Spinning reserve constraints(3) where maximum power output of the th thermal unit; maximum power output of the th hydro unit;Fig. 1. Simplified generation system under study.maximum increase in the output of the th thermal unit over one time interval; spinning reserve requirement at hour . c) Power output limits on thermal generating units—The problem model considers that the thermal generating units are on-line over the study period. units without prohibited operating zones:period is divided into time intervals and the short-term generation scheduling problem can be formulated as follows: (1) where(4) where total fuel cost of thermal units over the study period; fuel cost of the th thermal unit can be expressed as a quadratic function of real power generation, minimize power output of the th thermal unit; set of thermal units without prohibited operating zones. units with prohibited operating zones: where , , and are given. power output of the th thermal unit at hour ; number of considered time intervals under study; number of thermal units generated.(5) where set of thermal units with prohibited operating zones. d) Ramping response rate limits on thermal generating units (6) (7) wheresubject to the following. a) Power balance equations(2) wherewater-to-energy conversion function of the power plant associated with reservoir ; volume of water released from th reservoir for generation during hour ; number of reservoirs; power output of wind energy system at hour ;maximum decrease in the output of the th thermal unit over one time interval. e) Air emission limits on thermal generating units(8)LIANG AND LIAO: FUZZY-OPTIMIZATION APPROACH FOR GENERATION SCHEDULING1667where equivalent emission function of the th thermal unit generating power which is given by a second-order polynomialwhere forecast wind speed at hour ; cut in wind turbine speed; rated wind turbine speed; cut out wind turbine speed; where , , and are given. equivalent emission function of the th thermal unit generating power which is given by a first-order polynomial, wind-to-energy conversion function for wind power generation; equivalent rated power output for wind power generation. j) Power output limits on solar energy system where and are given. (13) (14) (15) where where , , and are given. solar radiation-to-energy conversion function of the PV generator given by [8]equivalent emission function of the th thermal unit generating power which is given by a second-order polynomial,total emission limit over the study period. f) Available water from each reservoir in storage for generation (9) where forecast total available water for the th reservoir over the study period. g) Bounds on water release from each reservoir in storage (10) where minimum volume of water released from the th reservoir; maximum volume of water released from the th reservoir. h) Bounds on each reservoir in storage III. GENERATION SCHEDULING BY PROPOSED APPROACH The system load demand, total available water, wind speed and solar radiation shown in (2), (9), (12), and (16) must be known before the generation scheduling problem can be performed. These values can only be known through short-term forecasting. Since system load demand, total available water, wind speed and solar radiation depend on the social behavior of customers and weather variables, etc., there are always errors in these forecast values. In this paper these forecast errors are considered as uncertainties. A characteristic feature of the proposed fuzzy-optimization approach is that the forecast errors can be taken into account using fuzzy sets. Fuzzy set formulation is used because we want a good solution to a model that represents the uncertainties inherent in a practical optimization problem. To solve generation scheduling using the proposed approach, membership functions for the total fuel cost, load demand, spinning reserve requirement, total available water, wind speed and solar radiation are first established [2], [16]. A genetic algorithm is then used to determine an optimal solution. (16) It is noted that PV cell temperature is neglected. forecast solar radiation at hour ; solar radiation in the standard environment set as 1000 ; a certain radiation point set as 150 ;equivalent rated power output of the PV generator; power charge/discharge to/from battery at hour ;(11) where minimum water volume of the th reservoir; maximum water volume of the th reservoir. i) Power output limits on wind energy system The power output function with respect to the wind speed is given by [8] or (12)1668IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 4, NOVEMBER 2007Fig. 2. Membership function for total fuel cost.Fig. 3. Membership function  for the load demand.A. Construction Membership Functions The membership functions are described as follows. 1) Membership Function for Total Fuel Cost: In the generation scheduling problem, the objective is to minimize the total fuel cost. We can define a membership function for the fuzzy set related to the total fuel cost so that a high cost is given a low membership value. By keeping the membership function as high as possible, a desirable solution with low generation cost for can be obtained. Fig. 2 depicts the membership function . In this figure, a membership or a satisthe fuzzy variable faction value of 1 is assigned to any that is less than zero. becomes larger than zero, the degree of satisfaction will As is expressed as decrease to zero. The membership function (17) where (18) average percentage error when the actual load is greater than the forecast load; average percentage error when the actual load is smaller than the forecast load; weighting factor. 3) Membership Function for Spinning Reserve Requirement: In general, utilities specify the required spinning reserve to ensure secure operations during outage events. A membership function as shown in Fig. 4 is employed. The membership is expressed as function (21) whereFig. 4. Membership function for spinning reserve requirement.where (20)weighting factor; cost tolerance factor in which its value is less than one; highest total fuel cost that include only thermal units to supply load. 2) Membership Function for Load Demand: It is noted from the power balance equations in (2) that the sum of the generation from thermal units, hydro units, wind and solar energy systems must be equal to the hourly load demand . In the generation denotes the hourly load demands in the scheduling problem, can only be obtained through load forefuture. Therefore, casting and there are always errors in the forecast hourly loads. as depicted in Fig. 3 is used. A membership function represents the percentage error in load forecasting. The memwhere no forecast error bership value will be 1 for values, the membership function deis observed. For other creases with increasing forecast error. The membership function can be written as (19)(22) (23)weighting factor. 4) Membership Function for Available Water: Just as in the case of hourly load demands, there are also errors in the forecastLIANG AND LIAO: FUZZY-OPTIMIZATION APPROACH FOR GENERATION SCHEDULING16696) Membership Function for Solar Radiation: Just as in the case of hourly load demands, there are also errors in the forecast that is similar to (19) solar radiation. A membership function is used. The membership function is formed as (28) where (29)Fig. 5. Membership function  for available water.average percentage error when the actual solar radiation is greater than the forecast solar radiation; average percentage error when the actual solar radiation is smaller than the forecast solar radiation; weighting factor.Fig. 6. GA chromosome structure.B. Fuzzy-Optimization Solution When the membership functions have been completed, an optimization method is then employed to solve the generation scheduling problem under uncertainties. A genetic algorithm (GA) [14], [15] is used on the optimization update procedure. GAs are general purpose optimization algorithms based on the mechanics of natural selection and genetics. GAs are an attractive alternative to other optimization approaches because of their robustness. When applying GAs to solve the generation scheduling problem, the problem physical decision variables on a GA chromosome must be addressed first. The control variables of the generation scheduling problem involve each thermal unit power output, water released volume for hydro units, wind speed for wind power generator and solar radiation for PV generator in each hour. The chromosome to the problem variables is formed as shown in Fig. 6. Here we use the following form to represent a chromosome in the GA search process . . . . . . .. . . . (30)available water. A membership function as depicted in Fig. 5 is employed. The membership function is expressed as follows. (24) where (25) average percentage error when the actual available water is greater than the forecast available water; average percentage error when the actual available water is smaller than the forecast available water; weighting factor. 5) Membership Function for Wind Speed: Just as in the case of hourly load demands, there are also errors in the forecast wind speed. A membership function that is similar to (19) is used. The membership function is formed as (26) where (27).. . .. . ..... . .where chromosome ;average percentage error when the actual wind speed is greater than the forecast wind speed; average percentage error when the actual wind speed is smaller than the forecast wind speed; weighting factor.number of GA chromosomes. After deciding the control variables on a GA chromosome, we then define that the evaluation of the so-called fitness function to assign a quality value to every solution produced. Since we consider the forecast errors under uncertain environment and the membership values can be obtained from the membership1670IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 4, NOVEMBER 2007functions described in (17), (19), (21), (24), (26), and (28) that represent the membership functions of total fuel cost, load demand, spinning reserve requirement, total available water, wind speed and solar radiation, respectively. to the problem, represented by Given a candidate solution a chromosome, the GA fitness function is computed as follows: (31) (32) (33) (34) (35) and (36) is defined as fitness function associated with the sowhere lution . For all candidate solutions , , the higher the fitness value of function represents the better the solution . Mathematically (37) The formulated optimization problem can be solved according to the following steps. Step 1: Initialize the control variables Randomly generate an initial population of size , i.e., initialize the control variables of GA chromosomes in (30). Step 2: Calculate the percentage errors for total available water, wind velocity, radiation and load demand; the spinning reserve and total fuel cost With the solutions obtained from above step in hand, we can: 1) use (25), (27), and (29) to calculate the percentage errors , , for total available water, wind velocity, and radiation, respectively. , wind energy system 2) calculate hydro unit power output power output and solar energy system power output for all . 3) use these all generating power output to obtain the load in (20) and use (22) and (1) to demand percentage errors calculate the spinning reserve and total fuel cost. Step 3: Calculate the membership values Use (17), (19), (21), (24), (26), and (28) to calculate the membership values of membership functions , , , , , for the total fuel cost, load demand, spinning reserve requirement, available water, wind speed and solar radiation, respectively. Step 4: Calculate the fitness value Use (31)–(36) to obtain ( ). In this paper, indicates the fitness function associated with the solution. The fitness function is a measure of the quality of each candidate solution. The higher the fitness value of function represents the better the solution . Solutions with high fitness values have a high probability of contributing new offspring to the next generation. Step 5: Create parent population Create parent (new) population using the following replacement policy: 1) If all offspring outperform every existing chromosome in the old population, then all of the offspring replace all existing chromosomes in the new population. 2) If only some offspring fare better than the existing population, they replace an equal number of existing chromosomes which are lowest in order of performance in the old population. Step 6 : Operate crossover and mutation Two chromosomes are selected from the parent population based on their fitness value. The selection rule used in this paper is a simple roulette-wheel selection [15]. The chromosomes from the two parents selected are combined to form new chromosomes that inherit segments of information stored in parent chromosomes. The crossover operator is usually used with a high probability. The mutation operator is usually used with a small probability. In this paper, the probabilities for crossover and mutation are set as 0.95 and 0.01, respectively. Step 7: Stop criterion The fitness function evaluation and genetic evolution take part in an iterative procedure which stops when a maximum number of generations is reached. Otherwise the process returns to step 2. IV. TESTING RESULTS The study system shown in Fig. 1 comprises ten thermal units, seven hydro units (with one pumped storage unit), one equivalent wind energy system and one equivalent solar energy system. The cost function data for the ten thermal units are given in Table VII. A battery is connected to the solar energy system to store energy. The forecast hourly load demand is shown in Fig. 7. The forecast total available water for the hydro plants is shown in Table I. The Ta-Kuan II hydro plant is a pumpedstorage plant. The forecast solar radiation and wind velocity data are shown in Table II. The spinning reserve requirement is set to 5% of the load demand. The fuzzy-optimization program was coded in Matlab and implemented on a PC-pentium IV computer. Now, the population size of the GA is set as 60. We have three cases to simulate. Case 1: Only the thermal generating system is used to supply load demands. Case 2: Thermal, hydro, wind energy and solar energy generating systems are connected to supply load demands. However, the battery is not connected to the solar energy system. Case 3: All generating systems, which include the battery, are connected to supply load demands.LIANG AND LIAO: FUZZY-OPTIMIZATION APPROACH FOR GENERATION SCHEDULING1671Fig. 7. Forecast hourly load. Fig. 8. Total thermal power output for case 1. TABLE I FORECAST TOTAL AVAILABLE WATER FOR THE HYDRO PLANTSTABLE II FORECAST SOLAR RADIATION AND WIND VELOCITYFig. 9. Total thermal power output for case 2.In case 1, only the thermal generating system (ten thermal units) is employed to supply load demands which are forecasted. There are always errors in these forecast loads. Therefore, we use the proposed fuzzy-optimization approach to solve the generation scheduling problem. The results include the de, the total fuel cost cision membership value , and the total emission equal to 96543 tons. Fig. 8 depicts the total thermal power output and forecast hourly load. It is observed from the results in Fig. 8 that the total thermal generation meets the load demands. In case 2, the generation system that includes ten thermal units, seven hydro units, one equivalent wind energy system and one equivalent solar energy system (without battery) is used to supply the load demands. In this case the load demands, total available water for hydro units, wind velocity for wind turbine generator and solar radiation for PV generator are forecast. There are always errors in these forecast values. Therefore, the proposed method is used to solve the problem. The results in, the total clude the decision membership valuefuel cost , and the total emissions equal to 89607 tons. Fig. 9 depicts the total thermal power output and forecast hourly load. The results in Fig. 9 show that the total thermal power output at the peak load period can be reduced by adding hydroelectric generation and renewable energy systems. The thermal power output at each hour is approximated. That is to say that the total fuel cost for the thermal generating units can be saved. In case 3, the generation system that includes all generating units (with battery) is used to supply load demands. The proposed method is employed to solve the problem. We get the , the total fuel cost decision membership value , and the total emission equals to 89404 tons. Fig. 10 depicts the total thermal power output and forecast hourly load. Fig. 11 illustrates the total hydro power output and wind energy system power output over the 24-h scheduling period. From Fig. 11 the hydro plants are operated to serve the peak load with hydro energy, which is usually referred to as the peak shaving operation. The negative hydro generating power outputs indicate the pumping operation for the Ta-Kuan II pumped-storage plant during the light load period. Fig. 12 depicts the individual generating curve for each hydro plant. Fig. 13 illustrates the solar energy system power output over the 24-h scheduling period. It is observed from Fig. 13 that the solar energy system is operated similar to the pumped-storage1672IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 4, NOVEMBER 2007Fig. 10. Total thermal power output for case 3.Fig. 13. Solar energy system power output for case 3.TABLE III SUMMARY OF DECISION MEMBERSHIP VALUE  AND TOTAL EMISSION., TOTAL FUEL COST,TABLE IV RESULTS FOR DIFFERENT GA POPULATION SIZESFig. 11. Total hydro power output and wind energy system power output for case 3.Fig. 12. Individual hydro generating power output for case 3.hydro plant since the battery is involved. The negative solar energy system power outputs indicate the charging operation for the battery during the light load period. , total fuel cost and total The decision membership value emission obtained from above-mentioned three cases are compared in Table III. From the results in Table III, the following observations can be made: 1) Using case 1, only thermal generating system is used to supply the load demands. The total fuel cost is the most expensive and the total emissions are also the highest, i.e., the air pollution is the most serious.2) Using case 2, the total fuel cost can be reduced by adding hydroelectric generation and renewable energy systems. . The total The total fuel cost is saved about NT$ 5.6 emission from case 2 is smaller than that from case 1. The air pollution is also lower. 3) Using case 3, both the total fuel cost and the total emission are the lowest among the three cases. The fuel energy for the thermal generating units can be significantly saved, and the air pollution emissions from the thermal generating units can be meaningfully reduced. 4) Using the proposed method for the generation scheduling problems for these three cases, the obtained decision membership values were higher. That represents satisfactory solutions. Now, we want to see the influence of different population sizes in the GA on the total fuel cost. Table IV shows the results from the decision membership value, total fuel cost and execution time for different population size from the proposed approach. Table IV shows that when the GA population size is increased, the total fuel cost will reduce. When it is increased to 100, the procedure will not find a better solution. However, the execution time will increase. In this paper the population size is set at 60. Because a population size of 60 results in a solutionLIANG AND LIAO: FUZZY-OPTIMIZATION APPROACH FOR GENERATION SCHEDULING1673TABLE V RESULTS FOR DIFFERENT COST TOLERANCE FACTORSTABLE VI SUMMARY RESULTS OF 20 SIMULATION TESTS Fig. 14. Convergence curves of the proposed approach with three different initial solutions.tests using the proposed fuzzy-optimization approach. This result shows that the proposed approach has the ability to find a good solution. The cost difference between the highest and lowest total fuel cost was only NT$ 9000. In other words, the difference is just only 0.122%. This result shows that the stability of the proposed approach to reach the final solution is very good. Note that the crisp value for the total fuel cost, obtained using a conventional GA under a crisp environment, i.e., without taking the uncertainties into account was NT$ . The reduction in total fuel cost was achieved by 74.41 taking advantage of the uncertainties in load, total available water, wind velocity, and solar radiation. Fig. 14 shows the convergence characteristics from the proposed approach with three different initial solutions among the 20 simulation tests. From the results in Fig. 14, the proposed approach has good ability to find a good solution, although the initial solutions are different. V. CONCLUSIONS A fuzzy-optimization approach was presented for solving the generation scheduling problem considering wind and solar energy systems. The generation scheduling problem was converted into a numerical formulation. A fuzzy-optimization approach was then proposed to solve the generation scheduling. To take the errors in forecast hourly load, total available water, wind velocity and solar radiation into account, membership functions were derived for the total fuel cost, the load demand, the spinning reserve requirement, the total available water, the wind velocity and the solar radiation using fussy set notations. With these membership functions, an approach for a genetic algorithm was presented to reach the optimal generation schedule under a fuzzy environment. To demonstrate the effectiveness of the proposed fuzzy-optimization approach, a simplified system consisting of ten thermal units, seven hydro units (with one pumped storage unit), one equivalent wind energy system and one equivalent solar energy system was performed. The results revealed that the proposed fuzzy-optimization approach is very effective in reaching an optimal generation schedule when imprecision is considered. We believe that the proposed fuzzy-optimization approach has good performance and the ability to determine the optimal solution. Moreover, the renewable energy sources will be important in the future. To efficiently utilize renewable energy, the total fuel cost ofthat is good and an execution time less than that from a population size of 80. Since the cost tolerance factor described in (18) was selected somewhat arbitrarily, it is desirable to see how different values will affect the final result. The decision membership value and total fuel cost of the optimal generation schedules obtained using different values are compared in Table V. From Table V, when is reduced, the total fuel cost will decrease. However, a lower results in a decision membership value that is too low. In practical applications, a decision membership that is too low is unacceptable because a small value value implies that the load demand, total available water, wind velocity and solar radiation level used are far from the actual load, available water, wind velocity and solar radiation. Therefore the value should be appropriately chosen to find a proper solution. In our work the value was set to 0.92. The initial solutions for the simulation tests were chosen somewhat arbitrarily. It is desirable to see how different initial solutions will affect the final results. We ran the proposed approach 20 times with different candidate solutions each time. Table VI shows the summary results from the 20 simulation。

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Determining Software Time-to-Market and Testing Stop Time when Release Time is a Change-Point[J]. International Journal of Mathematical, Engineering and Management Sciences,2020,5(2).[38]Ayushi Verma,Neetu Sardana,Sangeeta Lal. Developer Remendation for Stack Exchange Software EngineeringQ&A Website based on K-Means clustering and Developer Social Network Metric[J]. Procedia ComputerScience,2020,167.[39]Jagdeep Singh,Sachin Bagga,Ranjodh Kaur. Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques[J]. Procedia Computer Science,2020,167.[40]. Engineering - Software Engineering; Studies from Concordia University Update Current Data on SoftwareEngineering (On the impact of using trivial packages: an empirical case study on npm and PyPI)[J]. Computer Technology Journal,2020.[41]. Engineering - Software Engineering; Study Findings from University of Alberta Broaden Understanding of Software Engineering (Building the perfect game - an empirical study of game modifications)[J]. Computer Technology Journal,2020.[42]. Engineering - Software Engineering; Investigators at National Research Council (CNR) Detail Findings in Software Engineering [A Framework for Quantitative Modeling and Analysis of Highly (Re)Configurable Systems][J]. Computer Technology Journal,2020.[43]. Engineering - Knowledge Engineering; Data from University of Paris Saclay Provide New Insights into Knowledge Engineering (Dynamic monitoring of software use with recurrent neural networks)[J]. Computer Technology Journal,2020.[44]. Engineering - Circuits Research; Findings from Federal University Santa Maria Yields New Data on Circuits Research (A New Cpfsk Demodulation Approach for Software Defined Radio)[J]. Computer Technology Journal,2020.[45]. Soft Computing; Investigators from Lovely Professional University Release New Data on Soft Computing (An intensify Harris Hawks optimizer fornumerical and engineering optimization problems)[J]. Computer Technology Journal,2020.[46]. GlobalLogic Inc.; GlobalLogic Acquires Meelogic Consulting AG, a European Healthcare and Automotive-Focused Software Engineering Services Firm[J]. Computer Technology Journal,2020.[47]. Engineering - Circuits and Systems Research; Data on Circuits and Systems Research Described by Researchers at Northeastern University (Softcharge: Software Defined Multi-device Wireless Charging Over Large Surfaces)[J]. Telemunications Weekly,2020.[48]. Soft Computing; Researchers from Department of Electrical and Communication Engineering Report on Findings in Soft Computing (Dynamic Histogram Equalization for contrast enhancement for digital images)[J]. Technology News Focus,2020.[49]Mohamed Ellithey Barghoth,Akram Salah,Manal A. Ismail. A Comprehensive Software Project Management Framework[J]. Journal of Computer and Communications,2020,08(03).[50]. Soft Computing; Researchers from Air Force Engineering University Describe Findings in Soft Computing (Random orthocenter strategy in interior search algorithm and its engineering application)[J]. Journal of Mathematics,2020.[51]. Soft Computing; Study Findings on Soft Computing Are Outlined in Reports from Department of MechanicalEngineering (Constrained design optimization of selected mechanical system ponents using Rao algorithms)[J]. Mathematics Week,2020.[52]Iqbal Javed,Ahmad Rodina B,Khan Muzafar,Fazal-E-Amin,Alyahya Sultan,Nizam Nasir Mohd Hairul,Akhunzada Adnan,Shoaib Muhammad. Requirements engineering issues causing software development outsourcing failure.[J]. PloS one,2020,15(4).[53]Raymond C.Z. Cohen,Simon M. Harrison,Paul W. Cleary. 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Science - Social Science; Studies fromUniversity of Burgos Yield New Information about Social Science (Diagnosis of Software Projects Based on the Viable System Model)[J]. Computer Technology Journal,2020.[58]. Technology - Powder Technology; Investigators at Research Center Pharmaceutical Engineering GmbH Discuss Findings in Powder Technology [Extended Validation and Verification of Xps/avl-fire (Tm), a Computational Cfd-dem Software Platform][J]. Computer Technology Journal,2020.[59]Guadalupe-Isaura Trujillo-Tzanahua,Ulises Juárez-Martínez,Alberto-Alfonso Aguilar-Lasserre,María-Karen Cortés-Verdín,Catherine Azzaro-Pantel.Multiple software product lines to configure applications of internet of things[J]. IETSoftware,2020,14(2).[60]Eduardo Juárez,Rocio Aldeco-Pérez,Jose.Manuel Velázquez. Academic approach to transform organisations: one engineer at a time[J]. IET Software,2020,14(2).[61]Dennys García-López,Marco Segura-Morales,Edson Loza-Aguirre. 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New software for the optimization of the formulation and the treatment of dredged sediments for utilization in civil engineering[J]. Journal of Soils andSediments,2020(prepublish).[67]. Engineering - Concurrent Engineering; Reports from Delhi Technological University Add New Data to Findings in Concurrent Engineering (Systematic literature review of sentiment analysis on Twitter using soft puting techniques)[J]. Journal of Engineering,2020.[68]. Engineering; New Findings from Future University in Egypt in the Area of Engineering Reported (Decision support system for optimum soft clay improvement technique for highway construction projects)[J]. Journal of Engineering,2020.[69]Erica Mour?o,Jo?o Felipe Pimentel,LeonardoMurta,Marcos Kalinowski,Emilia Mendes,Claes Wohlin. On the performance of hybrid search strategies for systematic literature reviews in softwareengineering[J]. Information and SoftwareTechnology,2020,123.[70]. 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智能科学与技术专业英语

智能科学与技术专业英语

智能科学与技术专业英语一、单词1. Artificial Intelligence (AI)- 英语释义：The theory and development ofputer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision - making, and translation between languages.- 用法：“Artificial Intelligence” is often abbreviated as “AI” and can be used as a subject or in phrases like “AI technology” or “the field of AI”.- 双语例句：- Artificial Intelligence has made great progress in recent years. (近年来，人工智能取得了巨大的进展。

)- Manypanies are investing heavily in artificial intelligence research. (许多公司正在大力投资人工智能研究。

)2. Algorithm- 英语释义：A set ofputational steps and rules for performing a specific task.- 用法：Can be used as a countable noun, e.g. “T his algorithm is very efficient.”- 双语例句：- The new algorithm can solve the problem much faster. (新算法可以更快地解决这个问题。

外文文献文献列表

外文文献文献列表

- disruption ，: Global convergence vs nationalSustainable-，practices and dynamic capabilities in the food industry: A critical analysis of the literature5 Mesoscopic- simulation6 Firm size and sustainable performance in food -s: Insights from Greek SMEs7 An analytical method for cost analysis in multi-stage -s: A stochastic / model approach8 A Roadmap to Green - System through Enterprise Resource Planning (ERP) Implementation9 Unidirectional transshipment policies in a dual-channel -10 Decentralized and centralized model predictive control to reduce the bullwhip effect in -，11 An agent-based distributed computational experiment framework for virtual -/ development12 Biomass-to-bioenergy and biofuel - optimization: Overview, key issues and challenges13 The benefits of - visibility: A value assessment model14 An Institutional Theory perspective on sustainable practices across the dairy -15 Two-stage stochastic programming - model for biodiesel production via wastewater treatment16 Technology scale and -s in a secure, affordable and low carbon energy transition17 Multi-period design and planning of closed-loop -s with uncertain supply and demand18 Quality control in food -，: An analytical model and case study of the adulterated milk incident in China19 - information capabilities and performance outcomes: An empirical study of Korean steel suppliers20 A game-based approach towards facilitating decision making for perishable products: An example of blood -21 - design under quality disruptions and tainted materials delivery22 A two-level replenishment frequency model for TOC - replenishment systems under capacity constraint23 - dynamics and the ―cross-border effect‖: The U.S.–Mexican border’s case24 Designing a new - for competition against an existing -25 Universal supplier selection via multi-dimensional auction mechanisms for two-way competition in oligopoly market of -26 Using TODIM to evaluate green - practices under uncertainty27 - downsizing under bankruptcy: A robust optimization approach28 Coordination mechanism for a deteriorating item in a two-level - system29 An accelerated Benders decomposition algorithm for sustainable -/ design under uncertainty: A case study of medical needle and syringe -30 Bullwhip Effect Study in a Constrained -31 Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable -/ of perishable food32 Research on pricing and coordination strategy of green - under hybrid production mode33 Agent-system co-development in - research: Propositions and demonstrative findings34 Tactical ，for coordinated -s35 Photovoltaic - coordination with strategic consumers in China36 Coordinating supplier׳s reorder point: A coordination mechanism for -s with long supplier lead time37 Assessment and optimization of forest biomass -s from economic, social and environmental perspectives – A review of literature38 The effects of a trust mechanism on a dynamic -/39 Economic and environmental assessment of reusable plastic containers: A food catering - case study40 Competitive pricing and ordering decisions in a multiple-channel -41 Pricing in a - for auction bidding under information asymmetry42 Dynamic analysis of feasibility in ethanol - for biofuel production in Mexico43 The impact of partial information sharing in a two-echelon -44 Choice of - governance: Self-managing or outsourcing?45 Joint production and delivery lot sizing for a make-to-order producer–buyer - with transportation cost46 Hybrid algorithm for a vendor managed inventory system in a two-echelon -47 Traceability in a food -: Safety and quality perspectives48 Transferring and sharing exchange-rate risk in a risk-averse - of a multinational firm49 Analyzing the impacts of carbon regulatory mechanisms on supplier and mode selection decisions: An application to a biofuel -50 Product quality and return policy in a - under risk aversion of a supplier51 Mining logistics data to assure the quality in a sustainable food -: A case in the red wine industry52 Biomass - optimisation for Organosolv-based biorefineries53 Exact solutions to the - equations for arbitrary, time-dependent demands54 Designing a sustainable closed-loop -/ based on triple bottom line approach: A comparison of metaheuristics hybridization techniques55 A study of the LCA based biofuel - multi-objective optimization model with multi-conversion paths in China56 A hybrid two-stock inventory control model for a reverse -57 Dynamics of judicial service -s58 Optimizing an integrated vendor-managed inventory system for a single-vendor two-buyer - with determining weighting factor for vendor׳s ordering59 Measuring - Resilience Using a Deterministic Modeling Approach60 A LCA Based Biofuel - Analysis Framework61 A neo-institutional perspective of -s and energy security: Bioenergy in the UK62 Modified penalty function method for optimal social welfare of electric power - with transmission constraints63 Optimization of blood - with shortened shelf lives and ABO compatibility64 Diversified firms on dynamical - cope with financial crisis better65 Securitization of energy -s in China66 Optimal design of the auto parts - for JIT operations: Sequential bifurcation factor screening and multi-response surface methodology67 Achieving sustainable -s through energy justice68 - agility: Securing performance for Chinese manufacturers69 Energy price risk and the sustainability of demand side -s70 Strategic and tactical mathematical programming models within the crude oil - context - A review71 An analysis of the structural complexity of -/s72 Business process re-design methodology to support - integration73 Could - technology improve food operators’ innovativeness? A developing country’s perspective74 RFID-enabled process reengineering of closed-loop -s in the healthcare industry of Singapore75 Order-Up-To policies in Information Exchange -s76 Robust design and operations of hydrocarbon biofuel - integrating with existing petroleum refineries considering unit cost objective77 Trade-offs in - transparency: the case of Nudie Jeans78 Healthcare - operations: Why are doctors reluctant to consolidate?79 Impact on the optimal design of bioethanol -s by a new European Commission proposal80 Managerial research on the pharmaceutical - – A critical review and some insights for future directions81 - performance evaluation with data envelopment analysis and balanced scorecard approach82 Integrated - design for commodity chemicals production via woody biomass fast pyrolysis and upgrading83 Governance of sustainable -s in the fast fashion industry84 Temperature ，for the quality assurance of a perishable food -85 Modeling of biomass-to-energy - operations: Applications, challenges and research directions86 Assessing Risk Factors in Collaborative - with the Analytic Hierarchy Process (AHP)87 Random / models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage -s88 Information sharing and collaborative behaviors in enabling - performance: A social exchange perspective89 The coordinating contracts for a fuzzy - with effort and price dependent demand90 Criticality analysis and the -: Leveraging representational assurance91 Economic model predictive control for inventory ，in -s92 -，ontology from an ontology engineering perspective93 Surplus division and investment incentives in -s: A biform-game analysis94 Biofuels for road transport: Analysing evolving -s in Sweden from an energy security perspective95 -，executives in corporate upper echelons Original Research Article96 Sustainable -，in the fast fashion industry: An analysis of corporate reports97 An improved method for managing catastrophic - disruptions98 The equilibrium of closed-loop - super/ with time-dependent parameters99 A bi-objective stochastic programming model for a centralized green - with deteriorating products100 Simultaneous control of vehicle routing and inventory for dynamic inbound -101 Environmental impacts of roundwood - options in Michigan: life-cycle assessment of harvest and transport stages102 A recovery mechanism for a two echelon - system under supply disruption103 Challenges and Competitiveness Indicators for the Sustainable Development of the - in Food Industry104 Is doing more doing better? The relationship between responsible -，and corporate reputation105 Connecting product design, process and - decisions to strengthen global - capabilities106 A computational study for common / design in multi-commodity -s107 Optimal production and procurement decisions in a - with an option contract and partial backordering under uncertainties108 Methods to optimise the design and ，of biomass-for-bioenergy -s: A review109 Reverse - coordination by revenue sharing contract: A case for the personal computers industry110 SCOlog: A logic-based approach to analysing - operation dynamics111 Removing the blinders: A literature review on the potential of nanoscale technologies for the ，of -s112 Transition inertia due to competition in -s with remanufacturing and recycling: A systems dynamics mode113 Optimal design of advanced drop-in hydrocarbon biofuel - integrating with existing petroleum refineries under uncertainty114 Revenue-sharing contracts across an extended -115 An integrated revenue sharing and quantity discounts contract for coordinating a - dealing with short life-cycle products116 Total JIT (T-JIT) and its impact on - competency and organizational performance117 Logistical - design for bioeconomy applications118 A note on ―Quality investment and inspection policy in a supplier-manufacturer -‖119 Developing a Resilient -120 Cyber - risk ，: Revolutionizing the strategic control of critical IT systems121 Defining value chain architectures: Linking strategic value creation to operational - design122 Aligning the sustainable - to green marketing needs: A case study123 Decision support and intelligent systems in the textile and apparel -: An academic review of research articles124 -，capability of small and medium sized family businesses in India: A multiple case study approach125 - collaboration: Impact of success in long-term partnerships126 Collaboration capacity for sustainable -，: small and medium-sized enterprises in Mexico127 Advanced traceability system in aquaculture -128 - information systems strategy: Impacts on - performance and firm performance129 Performance of - collaboration – A simulation study130 Coordinating a three-level - with delay in payments and a discounted interest rate131 An integrated framework for agent basedinventory–production–transportation modeling and distributed simulation of -s132 Optimal - design and ，over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models133 The impact of knowledge transfer and complexity on - flexibility: A knowledge-based view134 An innovative - performance measurement system incorporating Research and Development (R&D) and marketing policy135 Robust decision making for hybrid process - systems via model predictive control136 Combined pricing and - operations under price-dependent stochastic demand137 Balancing - competitiveness and robustness through ―virtual dual sourcing‖: Lessons from the Great East Japan Earthquake138 Solving a tri-objective - problem with modified NSGA-II algorithm 139 Sustaining long-term - partnerships using price-only contracts 140 On the impact of advertising initiatives in -s141 A typology of the situations of cooperation in -s142 A structured analysis of operations and -，research in healthcare (1982–2011143 - practice and information quality: A - strategy study144 Manufacturer's pricing strategy in a two-level - with competing retailers and advertising cost dependent demand145 Closed-loop -/ design under a fuzzy environment146 Timing and eco(nomic) efficiency of climate-friendly investments in -s147 Post-seismic - risk ，: A system dynamics disruption analysis approach for inventory and logistics planning148 The relationship between legitimacy, reputation, sustainability and branding for companies and their -s149 Linking - configuration to - perfrmance: A discrete event simulation model150 An integrated multi-objective model for allocating the limited sources in a multiple multi-stage lean -151 Price and leadtime competition, and coordination for make-to-order -s152 A model of resilient -/ design: A two-stage programming with fuzzy shortest path153 Lead time variation control using reliable shipment equipment: An incentive scheme for - coordination154 Interpreting - dynamics: A quasi-chaos perspective155 A production-inventory model for a two-echelon - when demand is dependent on sales teams׳ initiatives156 Coordinating a dual-channel - with risk-averse under a two-way revenue sharing contract157 Energy supply planning and - optimization under uncertainty158 A hierarchical model of the impact of RFID practices on retail - performance159 An optimal solution to a three echelon -/ with multi-product and multi-period160 A multi-echelon - model for municipal solid waste ，system 161 A multi-objective approach to - visibility and risk162 An integrated - model with errors in quality inspection and learning in production163 A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge ，adoption in - to overcome its barriers164 A relational study of - agility, competitiveness and business performance in the oil and gas industry165 Cyber - security practices DNA – Filling in the puzzle using a diverse set of disciplines166 A three layer - model with multiple suppliers, manufacturers and retailers for multiple items167 Innovations in low input and organic dairy -s—What is acceptable in Europe168 Risk Variables in Wind Power -169 An analysis of - strategies in the regenerative medicine industry—Implications for future development170 A note on - coordination for joint determination of order quantity and reorder point using a credit option171 Implementation of a responsive - strategy in global complexity: The case of manufacturing firms172 - scheduling at the manufacturer to minimize inventory holding and delivery costs173 GBOM-oriented ，of production disruption risk and optimization of - construction175 Alliance or no alliance—Bargaining power in competing reverse -s174 Climate change risks and adaptation options across Australian seafood -s – A preliminary assessment176 Designing contracts for a closed-loop - under information asymmetry 177 Chemical - modeling for analysis of homeland security178 Chain liability in multitier -s? Responsibility attributions for unsustainable supplier behavior179 Quantifying the efficiency of price-only contracts in push -s over demand distributions of known supports180 Closed-loop -/ design: A financial approach181 An integrated -/ design problem for bidirectional flows182 Integrating multimodal transport into cellulosic biofuel- design under feedstock seasonality with a case study based on California183 - dynamic configuration as a result of new product development184 A genetic algorithm for optimizing defective goods - costs using JIT logistics and each-cycle lengths185 A -/ design model for biomass co-firing in coal-fired power plants 186 Finance sourcing in a -187 Data quality for data science, predictive analytics, and big data in -，: An introduction to the problem and suggestions for research and applications188 Consumer returns in a decentralized -189 Cost-based pricing model with value-added tax and corporate income tax for a -/190 A hard nut to crack! Implementing - sustainability in an emerging economy191 Optimal location of spelling yards for the northern Australian beef -192 Coordination of a socially responsible - using revenue sharing contract193 Multi-criteria decision making based on trust and reputation in -194 Hydrogen - architecture for bottom-up energy systems models. Part 1: Developing pathways195 Financialization across the Pacific: Manufacturing cost ratios, -s and power196 Integrating deterioration and lifetime constraints in production and - planning: A survey197 Joint economic lot sizing problem for a three—Layer - with stochastic demand198 Mean-risk analysis of radio frequency identification technology in - with inventory misplacement: Risk-sharing and coordination199 Dynamic impact on global -s performance of disruptions propagation produced by terrorist acts。

PSO研究现状综述

PSO研究现状综述

粒子群优化算法研究现状综述whitewatercoffee@Kennedy与Eberhart通过对鸟群觅食过程的分析并模拟，于1995年最先提出了原始的PSO算法[15][16]。

随后，Shi.Y和Eberhart分析了PSO算法的参数选择，并将一个重要的参数――惯性权重引入PSO[17][18]，并相继提出了线性递减惯性权重LDIW[18]、模糊惯性权重FIW[21]和随机惯性权重RIW[25]，文献[26]讨论了一类非线性惯性权重策略。

其他的研究者也提出了一些不同的惯性权重策略，这些改进的主要特点是：将可以表征算法优化进程的某些变量(如平均粒距、分布商等)，通过对惯性权重的自适应调节，以平衡算法的全局探测与局部开发能力[27][28][29]。

除了对惯性权重的关注外，文献[22][30]对加速度系数、边界条件等参数的设置，也进行了相关的研究。

在算法的数学分析上，与GA算法相比，PSO算法尚缺乏严格的数学基础。

粒子群系统本身是非线性离散时间系统，目前主要是将其简化为确定型线性离散时间系统，通过对单个粒子在一维或多维上的运动规律的分析，研究粒子运动的内在特性。

如Clerc对算法的参数选择和收敛性进行了初步的数学分析，引入收敛因子以保证算法的收敛[19]；Trelea在文献[31]中也对参数选择和收敛性做了一定的分析；Ozcan和Mohan对原始PSO算法进行了数学分析，指出停留在离散时间状态的粒子轨迹是连续的正弦波形，粒子不断从一个幅度与频率的正弦波跳跃到另外一个幅度与频率的正弦波上，如此在解空间中寻找更优值[32][33]。

与其它优化算法一样，PSO算法同样存在着收敛速度和收敛质量之间的矛盾。

为了获得更好的优化性能，研究者们通过借鉴其它优化技术的思想，提出了各种改进的PSO算法。

Angeline通过引入GA中的选择机制得到混合PSO(HPSO)[34]，该算法提高了收敛速度，但也增加了陷入局部极值的可能，尤其在优化Griewank函数时效果较差；Lovbjerg和Rasmussen等提出了具有繁殖和子群的杂交PSO算法[35]，将GA中的交叉机制引入PSO，由于后代选择并不是基于适应度值，防止了基于适应度值选择对那些多局部极值的函数优化时带来的潜在的问题，从理论上讲繁殖法可以更好地搜索粒子间的空间，这对于优化多模态函数很有利，但是对于单模态函数优化效果较差；文献[36]将变异机制引入PSO，随着迭代的进行对粒子位置施加线性递减的高斯扰动，有效的避免了粒子陷入局部极值的可能，同时扩大了粒子的搜索空间，提高了算法发现最优值的概率。