The Research On Improvement Of Monarch Butterfly Optimization And Ions Motion Algorithm

Monarch Butterfly Optimization(MBO)and Ions Motion Algorithm(IMO)both are two new swarm Intelligence stochastic optimization algorithms,which are proposed in 2015.But the two algorithms still have some defects,such as weak local search ability,low precision and premature convergence,and so on.Based on the background,this thesis studies on how to improve the performance of the two algorithms.The main research results of the dissertation are listed as follows:Firstly,aim at smoothing away the shortcomings of the weak global search ability,premature convergence and easily fall into local optimum,which exists in MBO,an improved Monarch butterfly optimization(IMBO)based on dynamic segmentation strategy is proposed.In the algorithm,in order to maintain the diversity of population search,the population is divided into two sub-groups dynamically,and different agents in different sub-group use different search strategy.The experimental results show that the proposed algorithm has stronger global search ability,better convergence speed and optimization precision in function optimization.Secondly,an MOIMBO is proposed,which is used to solve the multi-objective problems.Experimental results show the average performance of the MOIMBO in solving multi-objective problems is better than that of the PSO and the MBO.Secondly,aiming at overcoming the shortcomings of the ions motion algorithm(IMO),this paper proposed a new improved ions motion algorithm(IIMO).The IIMO depicts new mathematical models of ions motion based on the characteristics of the attraction between the same kind of ions and the repulsion between the different kinds of ions,and the random movement characteristics which ions appear in the liquid space.Experimental results show that the IIMO has faster convergence rate,stronger local search ability and global searching ability than that of the IMO and the PSO.The robustness of the IIMO is stronger than that of the IMO and the PSO.