A mutative-scale pseudo-parallel chaos optimization algorithm

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• 作者：Xiaofang Yuan (1)
  Xiangshan Dai (1)
  Lianghong Wu (2)
  
  1. College of Electrical and Information Engineering ; Hunan University ; Changsha ; 410082 ; People鈥檚 Republic of China
  2. College of Information and Electrical Engineering ; Hunan University of Science and Technology ; Changsha ; 411201 ; People鈥檚 Republic of China
  
• 关键词：Chaotic map ; Chaos optimization algorithm (COA) ; Parallel chaos optimization algorithm (PCOA) ; Cross operation ; Merging operation
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：19
• 期：5
• 页码：1215-1227
• 全文大小：1,278 KB
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• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479

文摘

Chaos optimization algorithms (COAs) utilize the chaotic map to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. Many existing applications show that COAs escape from the local minima more easily than classical stochastic optimization algorithms. However, the search efficiency of COAs crucially depends on appropriately starting values. In view of the limitation of COAs, a novel mutative-scale pseudo-parallel chaos optimization algorithm (MPCOA) with cross and merging operation is proposed in this paper. Both cross and merging operation can exchange information within population and produce new potential solutions, which are different from those generated by chaotic sequences. In addition, mutative-scale search space is used for elaborate search by continually reducing the search space. Consequently, a good balance between exploration and exploitation can be achieved in the MPCOA. The impacts of different chaotic maps and parallel numbers on the MPCOA are also discussed. Benchmark functions and parameter identification problem are used to test the performance of the MPCOA. Simulation results, compared with other algorithms, show that the MPCOA has good global search capability.