Improved Chicken Swarm Optimization Algorithm And Application Research

Chicken Swarm Optimization is a swarm intelligence optimization algorithm proposed by observing the daily life habits of chickens and simulating their hierarchical order and foraging behaviors,which has the advantages of clear structure,easy understanding,and easy implementation.However,the Chicken Swarm Optimization algorithm still has defects such as easy falling into local optimum,imbalance between local search ability and global search ability,slow convergence speed,and inability to solve multi-objective problems.In this paper,the defects and shortcomings of the Chicken Swarm Optimization algorithm are addressed.In order to improve the efficiency of the algorithm and expand the application scope of the algorithm,different versions of the Chicken Swarm Optimization algorithm are proposed and the improved algorithm is applied to function optimization,wireless sensor network coverage optimization,and multiobjective engineering applications.The main contents of this paper are as follows.(1)In order to solve the problem of poor population diversity in the later stage of the algorithm,a roulette strategy and a fireworks optimization algorithm are introduced.An Improved Chicken Swarm Optimization Algorithm based on The Fireworks Algorithm is proposed.The improved algorithm is applied to function optimization and compared with the other algorithms.The results prove that the performance of the improved algorithm is better than other algorithms.(2)In order to solve the imbalance problem of local search and global search in the Chicken Swarm Optimization algorithm,an adaptive population allocation strategy is introduced.In view of the fact that roosters and chicks are prone to fall into local optimum,the sine-cosine algorithm and random crossover mutation strategy are introduced.A Hybrid Chicken Swarm Optimization Algorithm is proposed.The Hybrid Chicken Swarm Optimization Algorithm is used for benchmark function tests and wireless sensor network applications.The results show that the improved algorithm has a strong optimization ability and greatly improves the network coverage.(3)Aiming at the inability of the Chicken Swarm Optimization algorithm to solve the multi-objective optimization problem,a fast non-dominated sorting strategy and a crowding distance strategy are introduced.To prevent the algorithm from falling into local optimum prematurely,an elite reverse learning strategy is introduced.A new Multi-objective Non-dominated Sorting Chicken Swarm Optimization Algorithm is proposed.To test the performance of the Multi-objective Non-dominated Sorting Chicken Swarm Optimization Algorithm,this algorithm is compared with other multi-objective optimization algorithms on multi-objective test functions and multi-objective engineering design problems.The demonstrated performance is better than other algorithms and can suggest more effective solutions for decision-makers.