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Research on Improved Genetic Algorithm for Low-dimensional and Multimodal function Optimization

Research on Improved Genetic Algorithm for Low-dimensional a...

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23rd Chinese Control and Decision Conference

作者： Xiao, Liqing Wang, Huaxiang Xu, Xiaoju Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China Xuzhou Inst Technol Dept Informat & Elect Engn Xuzhou 221111 Jiangsu Peoples R China

ISBN: (纸本)9781424487363

In order to overcome the disadvantages that simple genetic algorithm had in low-dimensional and multimodal function optimization, an improved algorithm was proposed. The new algorithm divided the search interval into different areas by method of orthogonal decomposition, produced initial populations that had different individual distribution density by using interval algorithm, and gave each individual an attribute that marked the area which it was in and guaranteed there was more than one individual in each area during the operating process of the algorithm. Particle swarm algorithm was introduced to the mutation operation of genetic algorithm to overcome the shortage of genetic algorithm, that was, the poor search ability in local areas, especially those that had low individual distribution density. When applied to six-hump camel back function and Branin RCOS function optimization, the simulation results show that, under the same experiment condition, in contrast to other improved algorithms, the new one improves the probability of searching to all extreme points, and has stronger ability of low-dimensional multi-modal function optimization, on the premise that the convergence precision is not affected.

关键词： Genetic Algorithm Interval Algorithm Particle Swarm Algorithm six-hump camel back function Branin RCOS function

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Research on Improved Genetic Algorithm for Low-dimensional and Multimodal function Optimization

Research on Improved Genetic Algorithm for Low-dimensional a...

引用

2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Liqing Xiao Huaxiang Wang Xiaoju Xu School of Electrical Engineering & Automation Tianjin UniversityNankai DistrictTianjin 300072Chin School of Electrical Engineering & Automation Tianjin UniversityNankai DistrictTianjin 300072Chin epartment of Information & Electrical Engineering Xuzhou Institute of Technology Xuzhou Jiangsu 221

In order to overcome the disadvantages that simple genetic algorithm had in low-dimensional and multimodal function optimization, an improved algorithm was proposed. The new algorithm divided the search interval into different areas by method of orthogonal decomposition, produced initial populations that had different individual distribution density by using interval algorithm, and gave each individual an attribute that marked the area which it was in and guaranteed there was more than one individual in each area during the operating process of the algorithm. Particle swarm algorithm was introduced to the mutation operation of genetic algorithm to overcome the shortage of genetic al gorithm, that was, the poor search ability in local areas, especially those that had low individual distribution density. When applied to six-hump camel back function and Branin RCOS function optimization, the simulation results show that, under the same experiment condition, in contrast to other improved algorithms, the new one improves the probability of searching to all extreme points, and has stronger ability of low-dimensional multi-modal function optimization, on the premise that the convergence precision is not affected.

关键词： Genetic Algorithm Interval Algorithm Particle Swarm Algorithm six-hump camel back function Branin RCOS function

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