Multi-objective particle swarm optimization with vector mutation and solution density information

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (21): 50-54.

• 研究、探讨 • Previous Articles Next Articles

Multi-objective particle swarm optimization with vector mutation and solution density information

PANG Shan1，YANG Xinyi2，SU Qingtang1

1.College of Information Science and Engineering，Ludong University，Yantai，Shandong 264025，China
2.Department of Aircraft Engineering，Naval Aeronautical and Astronautical University，Yantai，Shandong 264001，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-07-21 Published:2011-07-21

综合密度信息的矢量变异多目标粒子群优化

逄珊1，杨欣毅2，苏庆堂1

1.鲁东大学信息科学与工程学院，山东烟台 264025
2.海军航空工程学院飞行器工程系，山东烟台 264001

Abstract

Abstract:

For particle swarm optimization，when it is applied to multi-objective problems there may exist some problems concerning convergence，especially during the later part of iterations.Meanwhile the distribution of solutions needs to be enhanced.A novel multiple-objective particle swarm optimization algorithm is designed in order to solve the above problems.An integrated strategy of finding global best for particle in swarm is presented based on sigma method and solution density information.The proposed algorithm mutates the particles’ velocities by vector disturbance and helps the stagnated particles escape from local optima.The results show that the proposed algorithm has great advantages in convergence，distribution and diversity of solutions than classical algorithms.

Key words: multi-objective optimization, particle swarm optimization, evolutionary algorithm, mutation, density

摘要： 粒子群优化算法求解多目标优化问题存在早熟收敛和后期收敛速性差的不足，解的分布性也有待提高。为此设计一种新的多目标粒子群优化算法：对寻求粒子最优解的sigma方法进行改进，提出一种综合非支配解密度信息和sigma值的最优解求解机制。对变异粒子速度进行矢量扰动变异;对停滞粒子进行位置变异，有效避免算法的早熟收敛问题。测试结果表明，所提出的算法在收敛性和解的分布性、多样性方面较经典的算法具有明显的优势。

关键词: 多目标优化, 粒子群优化, 进化算法, 变异, 密度

PANG Shan1，YANG Xinyi2，SU Qingtang1. Multi-objective particle swarm optimization with vector mutation and solution density information[J]. Computer Engineering and Applications, 2011, 47(21): 50-54.

逄珊1，杨欣毅2，苏庆堂1. 综合密度信息的矢量变异多目标粒子群优化[J]. 计算机工程与应用, 2011, 47(21): 50-54.

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Multi-objective particle swarm optimization with vector mutation and solution density information

综合密度信息的矢量变异多目标粒子群优化

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