Improved Chicken Swarm Optimization Algorithm With Adaptive Features

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.