An Adaptive Hybrid Chicken Swarm-Particle Swarm Optimization Algorithm - XIAO Yuhe; LI Maojun; QI Zhan; LIU Fu (Kongzhi Yu Xinxi Jishu)
This paper presented a two-stage hybrid algorithm based on the improved chicken swarm algorithm and self-adjusted simplified particle swarm optimization(SASPSO) for the optimization of complex functions. Firstly, in order to solve the problems of poor global search ability and low convergence accuracy of the basic chicken swarm algorithm, a dynamic weight strategy and the global optimal individual are introduced into the chicken position updating formula, and a improved chicken swarm algorithm is proposed. Secondly, a diversity function is introduced to monitor the population diversity in real time, and a hybrid algorithm is formed by introducing SASPSO algorithm. Finally, 7 classical test functions are used to test the performance of the algorithm. The algorithm was compared with the basic particle swarm optimization, the basic chicken swarm optimization and the improved chicken swarm algorithm. The results show that the algorithm is suitable for solving complex function optimization problems and has good convergence speed and accuracy.