A modified gravitational search algorithm based on sequential quadratic programming and chaotic map for ELD optimization

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• 作者：XiaoHong Han (1)
  Long Quan (1)
  Xiaoyan Xiong (1)
  
  1. Key Laboratory of Advanced Transducers and Intelligent Control Systems ; Ministry of Education China ; Taiyuan University of Technology ; No. 79 ; West Yingze Street ; Taiyuan聽 ; 030024 ; China
  
• 关键词：Function optimization ; Heuristic search algorithm ; Swarm intelligence ; Gravitational search algorithm ; Sequential quadratic programming ; PieceWise Linear chaotic map
• 刊名：Knowledge and Information Systems
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：42
• 期：3
• 页码：689-708
• 全文大小：701 KB
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• 刊物类别：Computer Science
• 刊物主题：Information Systems and Communication Service
  Business Information Systems
  
• 出版者：Springer London
• ISSN：0219-3116

文摘

Gravitational search algorithm (GSA) is a stochastic search algorithm based on the law of gravity and mass interactions. For the purpose of enhancing the performance of standard GSA, this paper proposes a robust hybrid gravitational search algorithm (RHGSA). This algorithm makes the best of ergodicity of PieceWise Linear chaotic map to explore the global search while utilizing the sequential quadratic programming to accelerate the local search. To verify the performance of RHGSA, different types of benchmark functions including five unimodal functions and ten functions provided by CEC 2005 special session are tested in the experiments. Comparisons with other new variants of POS and GSA show that RHGSA obtains a promising performance on the majority of the test problems. Furthermore, a practical application problem, the economic load dispatch problem of power systems (ELD), is solved to further evaluate RHGSA. Compared with the previous evolutionary algorithms applied to ELD problem, RHGSA can get better results.