文献类型：期刊文献

中文题名：Locust Behaved Particle Swarm Optimization Technique

英文题名：Locust behaved particle swarm optimization technique

作者：Zhong, Wei-Min[1]; Xie, Xue-Qin[1]; Liang, Yi[1]; Luo, Na[1]; Zhang, Juan[1]; Qian, Feng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2014

卷号：31

期号：2

起止页码：190

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20143318074334);Scopus

基金：Major State Basic Research Development Program of China(No.2012CB720500);National Natural Science Foundations of China(Nos.61174118,21376077,61222303);the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)

语种：英文

中文关键词：evolutionary algorithm;particle swarm optimization(PSO);locust;collective behavior

外文关键词：Particle swarm optimization (PSO) - Population statistics

摘要：The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently,new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density,which is controlled by a serotonin called 5-hydroxytryptamine( 5HT). In this paper,based on the mechanism of the locusts' collective behavior,a new particle swarm optimization technique called LBPSO is studied. The number of swarms is selfadaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT,which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO,social particle swarm optimization( SPSO), improved SPSO and the standard particle swarm optimization( StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark( MPB)show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior.
The collective behavior of certain animals and insects has the characteristic of self-organization. The simple interactions among individuals can produce complex adaptive patterns at the level of the group. Recently, new scientific investigation pointed out that desert locusts show extreme phenotypic plasticity in transforming between the lonely phase and the swarming gregarious phase depending on the population density, which is controlled by a serotonin called 5-hydroxytryptamine (5HT). In this paper, based on the mechanism of the locusts' collective behavior, a new particle swarm optimization technique called LBPSO is studied. The number of swarms is self-adaptively adjusted by the acquired outstanding particles coming from behind the previous global best solution. The swarm sizes are related to the corresponding serotonin 5HT, which is determined by the optimization parameters such as global best and iteration number. And each swarm adopts one of three rules below according to its density, generalized social evolution strategy, generalized cognition evolution strategy and the independent moving strategy. A comparative study of LBPSO, social particle swarm optimization (SPSO), improved SPSO and the standard particle swarm optimization (StdPSO) on their abilities of tracking optima is carried out. And the results under four static benchmark functions and a dynamic function generator moving peaks benchmark (MPB) show that LBPSO outperforms the other three functions in both static and dynamic landscapes due to the introduced locusts' collective behavior. Copyright ? 2014.