Multi-objective Particle Swarm Optimization Algorithm Based On Angle Preference And Its Application

Whether in scientific research or social practice,there are optimization problems involving multiple objectives.When solving such problems,we often expect that all the objective can be optimal.However,in the process of solving practical problems,each goal of the multi-objective optimization problem may be mutually constrained,so we need to balance these objectives.The traditional method to solve the multi-objective optimization problem is to introduce parameters to transform it into a single objective optimization problem,and then solve it.However,due to the lack of prior knowledge,this kind of method is usually difficult to determine the parameter value,and has certain limitations,so the efficiency is relatively low.Particle swarm optimization(PSO)is an important branch of swarm intelligence optimization algorithm.It provides a new way to solve the multi-objective optimization problem.Aiming at the multi-objective optimization problem with high dimension and complex nonlinear characteristics,particle swarm algorithm comprehensively considers each objective,and proposes a large number of challenging research topics in the process of particle search and update,which further expands the research of multi-objective optimization problem.In this paper,based on the multi-objective optimization problem,the standard particle swarm optimization framework is used.By means of ε-Pareto domination,angle preference,mutation and three types of archive sets,two kinds of improved optimization algorithms are proposed.Simulation experiments are carried out on MATLAB,and the improved algorithms are applied to specific examples for verification and comparison experiments.The results show that the two algorithms are feasible and effective.The specific content and innovations can be summarized as follows:(1)The congestion degree determination,mutation operation and preference information are added to the multi-objective particle swarm algorithm,and the multi-objective particle swarm algorithm based on angle preference and ε-Pareto(AP-ε PSO)is proposed.On the basis of the AP-ε PSO algorithm,the archive set is updated into three types of archive sets,and Gaussian chaotic mutation is introduced,and the multi-objective particle swarm algorithm based on angle preference and three archive sets(AP-TPSO)is proposed.Finally,the two improved optimization algorithms are simulated on MATLAB.(2)The AP-ε PSO and AP-TPSO algorithms are combined with the Co-taxi pricing model respectively,to verify and compare on the specific multi-objective optimization problem.