In order to solve the problem that the basic chicken swarm optimization algorithm is premature convergence and easy to fall into the local optimum

an improved chicken swarm optimization algorithm with adaptive capability is proposed. Firstly

the initial population is constructed by the good point set

so that the initial solution can be uniformly distributed in the solution space and improve the diversity of the population. Then

according to the actual operation of the algorithm

the feeding speed factor and aggregation degree factor are introduced. And the inertia weight is expressed as a function of the two. Finally

the inertia weight is added to the position updating formula of the hen

so that the algorithm dynamically adjusts the inertia weight according to the actual running condition

Adaptability. Through the simulation experiments of eight benchmark functions and comparison with other algorithms

the results prove the superiority of improved chicken swarm optimization algorithm.