Mutation chicken swarm optimization based on nonlinear inertia weight
Kang Wang, LI Zhen-bi, Hao Cheng, Kun Zhang · 2017
Considering the problem that chicks only follow the hens to search for food and the roosters has weak global search capability in original chicken swarm optimization (CSO) algorithm, Mutation Chicken Swarm Optimization (MCSO) was proposed on the basis of Nonlinear Inertia Weight. The proposed algorithm first introduces the variation factor in the chicken particle position updating formula and then introduces the non-linear decreasing weight based on a parabola opening upwards in the rooster location update formula to improve the chicken location update formula and the rooster location update formula, thus the mutation chicken swarm optimization based on nonlinear inertia weight is easier to obtain the global optimal solution than original chicken swarm optimization algorithm. The optimization performance of the proposed algorithm was verified by six test function, the experimental results are compared with the chicken swarm optimization and differential evolution (DE) algorithm. Then, the mutation chicken swarm optimization based on nonlinear inertia weigh (NW-MCSO) was applied to the Anti-saccharification activity prediction. The experimental results show that the proposed algorithm has strong global search ability, high precision and better optimization performance compared with chicken swarm algorithm and differential evolution algorithm.