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Research of Multiobjective Particle Swarm Optimization Algorithm

Author: LiuLanXia
Tutor: WangJunNian
School: Hunan University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Multi-objective optimization Multi-objective evolutionary algorithm ε dominant Particle Swarm Optimization Multi- objective particle swarm optimization
CLC: TP301.6
Type: Master's thesis
Year: 2010
Downloads: 348
Quote: 2
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Abstract


Multi-objective optimization is optimization of the major research areas , the result is a set of solutions can not be compared with each other , a solution for a destination , it may be better , but for other targets may be poor in terms of . All of these solutions a collection of Pareto optimal solution set. The traditional method for solving multi-objective optimization problem , there are many defects, so how to design efficient optimization algorithms to solve multi-objective optimization problem becomes very urgent. Since the 1980s , the development of optimization algorithms to optimize the development of the theory provides new ideas and methods. Particle Swarm Optimization (PSO) algorithm is a recently developed a swarm intelligence algorithm . Its main features are: groups of individual particles according to their own experience and the experience of other particles to obtain valid information to guide the search. Since the algorithm is simple, fast convergence , etc., since it has been raised widespread concern , is now widely used function optimization, neural networks, fuzzy systems control , pattern recognition , showing a strong vitality. In this paper, multi-objective particle swarm optimization study is proposed based on ε dominant objective particle swarm optimization algorithm . ε dominant concept was originally developed by Deb et al , using the set ε parameter values ??to obtain the required number of Pareto optimal solutions as well as the entire Pareto optimal surface is divided into multiple hypercube , each cube there are up to a non-dominant individual, thus maintaining the distribution of the obtained solutions . Same article also orthogonal design method to generate the initial population , thereby enhancing the algorithm 's ability to use the initial population . Finally, test functions with several classic experiments verified by comparing the proposed algorithm in time efficiency , distributed degrees , CPU running time and other aspects of a good performance.

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