Chicken Swarm Optimization Algorithm Based on Behavior Feedback and Logic Reversal
Chicken Swarm Optimization Algorithm Based on Behavior Feedback and Logic Reversal

Considering the problem that a rooster in chicken swarm optimization (CSO) easily falls into a local optimum and cannot fully demonstrate the population wisdom, the paper proposed an improved CSO algorithm, which based on behavior feedback from hens to rooster and rooster behavior logic reversal, therefore it is named behavior feedback and logic reversal CSO (BFLRCSO). The proposed algorithm changes the original rooster behavior logic to boost the convergence rate, which can accelerate the rooster optimization process, and the algorithm also introduces a feedback mechanism from hens to rooster which can prevent swarm dropping into a local optimum. The experiment results demonstrated that the BFLRCSO algorithm is not easy to fall into a local optimum, which has a better optimization result and shorter optimization time compared with the original CSO algorithm in both high and low dimensional search space.
Considering the problem that a rooster in chicken swarm optimization (CSO) easily falls into a local optimum and cannot fully demonstrate the population wisdom, the paper proposed an improved CSO algorithm, which based on behavior feedback from hens to rooster and rooster behavior logic reversal, therefore it is named behavior feedback and logic reversal CSO (BFLRCSO). The proposed algorithm changes the original rooster behavior logic to boost the convergence rate, which can accelerate the rooster optimization process, and the algorithm also introduces a feedback mechanism from hens to rooster which can prevent swarm dropping into a local optimum. The experiment results demonstrated that the BFLRCSO algorithm is not easy to fall into a local optimum, which has a better optimization result and shorter optimization time compared with the original CSO algorithm in both high and low dimensional search space.