Improved Bat Algorithm Based on Multipopulation Strategy of Island Model for Solving Global Function Optimization Problem. (18th August 2019)

• Record Type: Journal Article
• Authors: Guo, Sha-Sha; Wang, Jie-Sheng; Ma, Xiao-Xu
• Other Names: Alonso-Betanzos Amparo Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2019(2019)
• Publication Date: 2019-08-18
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2019/6068743
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The bat algorithm (BA) is a heuristic algorithm that globally optimizes by simulating the bat echolocation behavior. In order to improve the search performance and further improve the convergence speed and optimization precision of the bat algorithm, an improved algorithm based on chaotic map is introduced, and the improved bat algorithm of Levy flight search strategy and contraction factor is proposed. The optimal chaotic map operator is selected based on the simulation experiments results. Then, a multipopulation parallel bat algorithm based on the island model is proposed. Finally, the typical test functions are used to carry out the simulation experiments. The simulation results show that the proposed improved algorithm can effectively improve the convergence speed and optimization accuracy.