优化混合蛙跳算法的WSN三维定位方法刘宏;王其涛;夏未君【期刊名称】《计算机工程与应用》【年(卷),期】2017(053)002【摘要】根据传统混合蛙跳算法（Shuffled Frog Leaping Algorithm，SFLA）收敛速度较慢、局部最优的不足，提出了优化混合蛙跳算法（Optimized Shuffled Frog Leaping Algorithm，OSFLA），并将其应用于无线传感器网络（WSN）节点三维定位。
在三维定位中运用极大似然法进行粗略定位，对锚节点进行加权处理，设定搜索区域，再使用优化蛙跳算法进行迭代求精。
仿真实验结果表明：优化混合蛙跳算法（OSFLA）比混合蛙跳算法（SFLA）具有更高的收敛速度和定位精度，同时更加适合于锚节点数较少场合。
且在三维定位中与常用的几种算法相比OSFLA算法在定位精确度和稳定性方面都具有一定的提高。
%According to the traditional Shuffled Frog LeapingAlgorithm(SFLA)that its convergence speed is slow and its local optimum has the shortcomings. The Optimized Shuffled Frog LeapingAlgorithm(OSFLA)is put forward here and applied to the three-dimensional positioning of the wireless sensor network node. In the three-dimensional positioning, at first, using maximum likelihood has a rough positioning, then having weighted processing for anchor nodes and setting the search area. Finally, using OSFLA achieves the effect of iterative refinement. The simulation result shows that, the OSFLA has higher convergence speed and precision than the traditional SFLA. At the same time, it is more suitable forthe occasion that has the less number of anchor node. Besides, in the three dimensional positioning, compared with the commonly used several kinds of algorithms, the OSFLA algorithm’s positioning accuracy and stability of OSFLA algorithm are obviously improved.【总页数】6页(P129-133,140)【作者】刘宏;王其涛;夏未君【作者单位】江西理工大学电气工程与自动化学院，江西赣州 341000;江西理工大学电气工程与自动化学院，江西赣州 341000;江西理工大学电气工程与自动化学院，江西赣州 341000【正文语种】中文【中图分类】TP393【相关文献】1.基于PSO优化LSSVR的三维WSN节点定位方法 [J], 张烈平;陈鸣;季文军;2.基于WSNs三维无线层析成像定位方法研究 [J], 黄炳华; 王满意; 刘增鑫3.基于跳距修正与狮群优化的WSNs三维定位算法 [J], 苟平章;刘学治;孙梦源;何博4.基于果蝇优化的WSNs三维节点定位算法 [J], 王海云;李洁;张姣5.带混沌映射的三维WSN蜂群优化定位算法 [J], 李田来;刘方爱因版权原因，仅展示原文概要，查看原文内容请购买。
改进混合蛙跳算法的云计算资源调度张沫【摘要】Reasonable scheduling of resource is the current research focus in cloud computing environment.Aiming at the shortage of shuffled frog leaping algorithm,we propose a novel cloud computing resource scheduling strategy which is based on improved shuffled frog leaping algorithm (ISFLA).First it introduces the particle update concept to local optimisation process to accelerate convergence rate,then it exerts chaos disturbance on best individual in global optimisation to reduce the probability of local optimum occurrence,finally the simulation experiment is carried out on CloudSim platform.Result shows that the proposed algorithm reduces the completion time of cloud computing task with more reasonable load distribution of resources.%资源合理调度是云计算研究热点。
针对混合蛙跳算法不足，提出一种改进混合蛙跳算法的云计算资源调度策略（ISF-LA）。
带有选择和自适应变异机制的混合蛙跳算法刘悦婷【摘要】混合蛙跳算法易陷入局部最优,且收敛速度较慢.为此,提出一种带有选择和自适应变异机制的蛙跳算法.引入线性递减的动态惯性权重修正最差青蛙,按照一定的概率选择适应度值较优的青蛙代替较差青蛙,并对每只青蛙个体以不同概率进行自适应变异.仿真结果表明,该算法可以平衡全局搜索和局部搜索,寻优能力强、迭代次数少,解的精度较高,更适合高维复杂函数的优化.%Because of the problems of Shuffled Frog Leaping Algorithm(SFLA) such as local optimality and slow convergence rate, a leapfrog algorithm with selection and adaptive mutation mechanism is presented. This algorithm introduces the linear decreasing adaptive inertia weight to correct the poor frog update strategy. It selects the frog with better fitness value to substitute the poor, and makes very frog adaptively mutate with different probability. Simulation results show that this algorithm can balance the global search and local search, and its optimization ability is stronger, the number of iterations is less, the solution is better, and the suitable for high-dimensional optimization of complex functions is more.【期刊名称】《计算机工程》【年(卷),期】2012(038)023【总页数】6页(P206-210,218)【关键词】混合蛙跳算法;选择机制;自适应变异;惯性权重;更新策略;全局最优【作者】刘悦婷【作者单位】甘肃联合大学电子信息工程学院,兰州730000【正文语种】中文【中图分类】TP3911 概述混合蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)是一种群体智能优化算法[1]。
一种蛙跳和差分进化混合算法何兵;车林仙;刘初升【期刊名称】《计算机工程与应用》【年(卷),期】2011(047)018【摘要】Shuffled Leaping Frog Algorithm(SFLA) is characterized by simplicity, few control parameters required,and easily be used,but has the disadvantages of premature convergence and low precision for hard high-dimensional optimization problems,due to its rapid loss of the population diversity and the lack of local refined search abilities at the later stages of generations. In order to overcome the easy premature or early convergence of SFLAs,this paper hybridizes the SFLA and the Differential Evolution(DE) algorithm to form a hybrid optimization algorithm,namely SFL-DE,which borrows the idea from DE/best/1/bin strategy that has the advantages of strong global search ability and better population diversity. Comparisons are presented to test performances of the new algorithm employing 6 benchmark 30-dimensional functions. Compared with SFLA and standard DE(i.e.,DE/best/1/bin and DE/rand/l/bin schemes) algorithms,the experimental results in terms of the global optimization efficiency,the solution accuracy and the computation robustness demonstrate that the SFL-DE algorithm is a better tool for solving some benchmark optimization problems within a few fixed generations,but takes a longer run time.%混洗蛙跳算法(SFLA)具有算法简单、控制参数少、易于实现等优点,但在高维难优化问题中算法容易早熟收敛且求解精度不高.导致该缺陷的主要原因是在进化后期种群多样性迅速下降,且缺乏局部细化搜索能力.借鉴差分进化算法(DE)中DE/best/1/bin版本具有全局搜索能力较强、种群多样性较好的优点,将SFLA与DE有机融合,形成混合优化算法(SFL-DE),以克服SFLA容易早熟收敛的缺陷.给出了6个30维benchmark问题数值对比实验,结果表明,在给定的较小进化代数内,SFL-DE的寻优效率、计算精度、鲁棒性等性能优于SFLA和基本DE(DE/best/l/bin和DE/rand/1/bin),不足之处是其耗时更长.【总页数】5页(P4-8)【作者】何兵;车林仙;刘初升【作者单位】中国矿业大学,机电工程学院,江苏徐州,221008;泸州职业技术学院,机电工程研究所,四川泸州,646005;中国矿业大学,机电工程学院,江苏徐州,221008;泸州职业技术学院,机电工程研究所,四川泸州,646005;中国矿业大学,机电工程学院,江苏徐州,221008【正文语种】中文【中图分类】TP391【相关文献】1.一种改进的粒子群与差分进化混合算法 [J], 任雪婷;贺兴时2.基于人工鱼群和蛙跳混合算法的光伏阵列多场景参数辨识 [J], 徐岩;高兆;朱晓荣3.一种差分进化和模拟退火粒子群混合算法 [J], 杜松;周健勇4.一种混沌差分进化和粒子群优化混合算法 [J], 阳春华;钱晓山;桂卫华5.一种粒子群和改进自适应差分进化混合算法及在生产调度中的应用 [J], 周艳平;蔡素;李金鹏因版权原因，仅展示原文概要，查看原文内容请购买。
