Research And Application Of Multi-objective Particle Swarm Optimization Algorithm

Many optimization problems can be classified as multi-objective optimization problems in real life.Single-objective optimization can find the only optimal feasible solution.Unlike single-objective optimization,the multi-objective optimization problem needs to find a solution set that can make each sub-object as optimal as possible.As an intelligent algorithm,the particle swarm optimization algorithm is widely used in solving multi-objective optimization problems because of its simple form,flexible parameter setting,simple and easy operation,and rapid convergence and multiple solutions at one time.However,the particle swarm optimization algorithm also has some shortcomings,such as converging slowly and falling into local optimum easily.In order to converge quickly and accurately,this paper mainly studies the shortcomings of particle swarm optimization algorithm in solving multi-objective optimization problems,such as converging slowly and falling into local optimum easily.Aiming at the above problems,this paper proposed an improved particle swarm optimization algorithm based on population partitioning,mutation strategy and self-adaptive selection strategy,which was applied to the optimization problem of logistics Web service composition.This paper makes the following research: Firstly,the population is divided into some small groups based on the traditional particle swarm optimization.At the same time,different mutation strategies and inertia weights are set for different sub-populations to balance search capabilities and improve the search efficiency of the algorithm.Then,the global and the individual optimal solutions are selected by the self-adaptive selection strategy to ensure that the algorithm converges to the real Pareto front.Finally,the improved multi-objective particle swarm optimization algorithm is applied to the logistics Web service composition,and the experiments show that the algorithm is effective and feasible.