关键词: 群体智能, 鸡群算法, 遗传, 交叉算子, 变异算子

Abstract: Aiming at the problems of slow convergence speed, low optimization precision and easy to fall into local optimum when solving high dimensional complex problems, combined with genetic thought, an improved chicken swarm optimization algorithm is proposed by adding a new concept that roosters and hens mate, mutate to hatch out new chicks, setting the mating cycles and the chicks update cycles, and using crossover operator and mutation operator to improve. Experiments are conducted on the 10 benchmark functions and the results show that the improved chicken swarm optimization algorithm has obviously better performance in optimization precision, the solution accuracy, convergence speed, stability and robustness than chicken swarm optimization algorithm and other two algorithms.

Key words: swarm intelligence, chicken swarm optimization, genetic, crossover operator, mutation operator