Research And Application Of Multi-Objective Particle Swarm Optimization Algorithm Using Large Scale Variable Decomposition

Multi-objective particle swarm optimization algorithm is of better convergence,easier calculation and less parameter settings that is very popular among engineering practice and multi-objective optimization research.However,“variable dimensionality” will be triggered as decision variables increase.The multi-objective optimization of problems with large scale variable has become a focus in multi-objective evolutionary algorithm research field.Existing large-scale variable decomposition research focused on single objective problem,and the study in multi-objective optimization problem is very little.Besides,there are non-separable variables in large scale variables.If we ignore the intrinsic link,the linkage variables will affect the quality of the solution Set.The current variable decomposition method is mainly based on fixed decomposition which is lack of effective way to tap the variable related information.To solve these two difficult issues,this paper proposes periodic random variable random decomposition strategy to break down the large scale variable space,and take advantages of multi-objective particle swarm optimization,to search multi-objective particle swarm optimization algorithm using large scale variable decomposition and its applications.The main contributions of this paper are as follows:1.The article will deeply search decision variables decomposition method in multi-objective optimization problems combining dominant mechanism and mathematical characteristics,and introduces the cooperative co-evolution to solve multi-objective optimization problems.2.This paper proposes random variable decomposition strategy,i.e.to promote the possibility of distributing associated variables into one group by random variable decomposition on the basis of variable groups,so as to realize better maintenance in association between variable groups.CCMOPSO is proposed through the integration of cooperative co-evolution evolutionary frame into the large scale variable decomposition.Comparative simulation experiment is conducted after the variable extension on typical standard functions of ZDT1,ZDT2,ZDT3,DTLZ1 and DTLZ2.Comparison between convergence and diversity of the algorithm with the binary addition index ? and hyper-volume indicator(HV),shows this algorithm is of better diversity,convergence and easiness in multi-objective function with large scale variable than MOPSO,NSGA-II MOEA/D and GDE3,and computational complexity is decreased.3.To solve wireless sensor network coverage control problem,CCMOPSO is proposed through the integration of cooperative co-evolution evolutionary frame into the large scale variable decomposition.This algorithm was applied to the engineering problem of wireless sensor network coverage control,and takes the improvement of energy using efficiency as important index.It realizes energy consumption decrease and node energy equilibrium.The comparison result showed the effectiveness of CCMOPSO.