An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems

Liang, Jianhui; Wang, Lifang; Ma, Miao

doi:10.1155/2022/5359732

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…In light of the fitness function values, the whole chicken swarm is divided into the roosters, hens, and chicks, where roosters have the best fitness values, hens take second place, and chicks have the worst fitness values. The algorithm relies on the roosters, hens, and chicks to constantly conduct information interaction and cooperation sharing and finally finds the best food source [ 30 , 31 ]. The characteristics are as follows:…”

Section: The Basic Cso Algorithmmentioning

confidence: 99%

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An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization

Liang

Wang

2023

Biomimetics

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With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.

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Section: The Basic Cso Algorithmmentioning

confidence: 99%

“…In Table 2 , the parameters of AFSA are set after trial and error on the basis of the literature [ 31 ]. The parameter of ABC is set according to the study [ 34 ] where ABC has been proposed.…”

Section: Simulation Experiments and Analysismentioning

confidence: 99%