A Memetic Particle Swarm Optimization Algorithm to Solve Multi-objective Optimization Problems

2017 13th International Conference on Computational Intelligence and Security (CIS) Pub Date : 2017-12-01 DOI:10.1109/CIS.2017.00018

Xin Li, Jingxuan Wei, Yang Liu

引用次数: 4

Abstract

An improved particle swarm optimization algorithm to solve multi-objective optimization problems is proposed, called MoMPSO. Firstly, Simulated annealing is incorporated to Particle swarm optimization to enhance the search ability. Secondly, Nonuniform mutation is used to increase the diversity. The simulated results show that the proposed algorithm is better than the compared ones.

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求解多目标优化问题的模因粒子群算法

提出了一种求解多目标优化问题的改进粒子群算法，称为MoMPSO。首先，在粒子群算法中引入模拟退火，增强粒子群算法的搜索能力;其次，利用非均匀突变增加多样性。仿真结果表明，本文提出的算法优于其他算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2017 13th International Conference on Computational Intelligence and Security (CIS)

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