Based On Hybrid Animal Migration Algorithm For Solving Global Optimization Problems

Animal Migration Optimization algorithm(AMO), as a swarm intelligent algorithm, wasproposed in2013,based on animal migration model for solving more complex optimizedproblems with continuous variables.From the result of simulation experiment in matlab,AMO outperforms some heuristic optimization algorithms in solving some globaloptimization problems,which is meaningful in scientific research and wide applications. Thepaper aims at improving the capability of obtaining the global solution and having betterconvergence rate.In the search,a new hybrid algorithm is proposed as PSAMO bycombining AMO and PSO dealing with global optimization problems.To verify theperformance of PSAMO,we applied it to23standard benchmark functions,which proves itseffectiveness and feasibility.(1) In order to improve the capability of gaining obtaining the global solution andhaving better convergence rate, the strategies for revision lies in adjusting parameter “w” tohave enough global search capability in PSAMO.(2) Setting up a simulation experiment in matlab using23benchmark functions andcomparing the result of mean of optimal solution with some other optimization algorithmslike PSO,FA,CS.etc.(3) From the analysis of the results we can prove that PSAMO outperforms somealgorithms in convergence rate and gains better global solution than other algorithms. Inconclusion, PSAMO outperforms AMO to some degree in the capability of global search.