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The Research on Particle Swarm Optimization Algorithm to Solve Multi-Objective Optimization Problem

Author: SongWu
Tutor: ZhengJinHua
School: Xiangtan University
Course: Computer Software and Theory
Keywords: Multi-objective optimization Multi - objective evolutionary algorithm Particle Swarm Optimization Multi-objective particle swarm optimization
CLC: TP301.6
Type: Master's thesis
Year: 2007
Downloads: 798
Quote: 7
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In recent years , evolutionary computation in dealing with complex, nonlinear problems achieved greater success . Especially for multi-objective optimization problem , there have been many multi-objective optimization algorithm ( MOEA) , the most representative algorithm NSGA2 and SPEA2 time you run these algorithms can be multiple Pareto optimal solutions . Kennedy and Eberhart in 1995 put forward a new optimization algorithm - Particle Swarm Optimization (PSO), this new algorithm inspired groups of species of birds , insects , fish and other prey behavior . Due to its simple and effective , followed by widespread concern , at the same time its good characteristics manifested in solving the single- objective optimization problem is also very suitable for solving multi -objective optimization problem . Particle swarm algorithm for solving multi-objective optimization problems at home and abroad have been part of the research , but they all have certain deficiencies : on the one hand, the poor performance of the distribution of the solution set the other hand, is a high-dimensional target convergence is not good. In the original basis of the results , through the use of a new global extremum selection and adding a new mutation operator to accelerate the convergence speed. To improve the distribution of the performance of the algorithm , we propose a particle swarm optimization strategy based on the density of the external set of remains , when greater than the size of the external set of non - dominating set , using the method of density trim . Not converge on the issue of high-dimensional problem , we use a combination of a mixed decision Pareto ranking , two decision table , a decision table is randomly generated , the value of a decision table is fixed through the decision to select a non-dominated solutions , and out of being dominated solutions , external set of eventually gaining on the optimal surface . Using a series of standard test function experiments , the experimental results show that our algorithm is very effective , effective way to solve the high dimensional multiobjective optimization problems .

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