Apical-dominant particle swarm optimization

作     者：Zhihua Cui~(a,b) Xingjuan Cai~b Jianchao Zeng~b Guoji Sun~a a State Key Laboratory for Manufacturing Systems Engineering,Xi’an Jiaotong University,Xi’an 710049,China b Division of System Simulation and Computer Application,Taiyuan University of Science and Technology,Taiyuan 030024,China 

作者机构：[a]State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an 710049 China [b]Division of System Simulation and Computer Application Taiyuan University of Science and Technology Taiyuan 030024 China 

出 版 物：《Progress in Natural Science:Materials International》 (自然科学进展·国际材料(英文))

年 卷 期：2008年第18卷第12期

页      面：1577-1582页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (Grant No.60674104) 

主　　题：Apical-dominance phenomenon Particle swarm optimization Branch growth model High-dimensional multi-modal benchmarks 

摘      要：Particle swarm optimization (PSO) is a new stochastic population-based search methodology by simulating the animal social behaviors such as birds flocking and fish *** improvements have been proposed within the framework of this biological assumption. However,in this paper,the search pattern of PSO is used to model the branch growth process of natural *** provides a different poten- tial manner from artificial *** illustrate the effectiveness of this new model,apical dominance phenomenon is introduced to construct a ncvel variant by emphasizing the influence of the *** this improvement,the population is divided into three different kinds of buds associated with their ***,a mutation strategy is applied to enhance the ability escaping from a local ***- ulation results demonstrate good performance of the new method when solving high-dimensional multi-modal problems.