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A multi-objective particle swarm optimization algorithmbased on Pareto correlation degree domination(PDF)


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A multi-objective particle swarm optimization algorithmbased on Pareto correlation degree domination
Tang Kezong1Li Zuoyong2Zhan Tangsen1Li Fang1Jiang Yunhao1
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 2.Industrial Robot Application of Fujian University Engineering Research Center,Minjiang University,Fuzhou 350108,China
multi-objective optimization particle swarm optimization Pareto domination correlation degree diversity
In order to improve the convergence and diversity of multi-objective optimization algorithm,this paper proposes a multi-objective particle swarm optimization algorithm based on Pareto correlation degree domination(MOPSO-PCD). On the basis of strict compliance with the traditional Pareto domination programming,MOPSO-PCD integrates the grey relational analysis method into the evolutionary process of non-dominated solutions,and designs a new Pareto correlation association degree domination programming. In the selection process of the global optimal particle,the optimal particle with the largest correlation degree leads the particle swarm to approach the true Pareto frontier distribution. At the same time,the domination programming can also maintain the diversity of the non -dominated solutions in the external archive,and reduce or avoid the loss of the diversity of the final solution set,thus maintaining the distribution process of the non-dominated solutions of the external archive. Simulation results of ZDT and DTLZ test functions show that MOPSO-PCD has better Pareto optimal frontier distribution and faster convergence efficiency than three comparison algorithms.


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Last Update: 2019-09-30