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Locust Behaved Particle Swarm Optimization Technique

期刊名称：JoumaI of Donghua University
时间：2014
页码：207-211
分类：TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]
作者机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
相关基金：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）
相关项目：化工过程控制与优化

作者： Yi Liang（梁毅）|Na Luo（罗娜）|Juan Zhang（张娟）|Feng Qian（钱锋）|

中文摘要：

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.

英文摘要：

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 swann 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 swann 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 swann optimization （ SPSO ）, improved SPSO and the standard particle swann 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.