An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems. (22nd November 2022)

• Record Type: Journal Article
• Authors: Liang, Jianhui; Wang, Lifang; Ma, Miao
• Other Names: Zhou Miaolei Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-11-22
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/5359732
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.