A Novel Hybrid Particle Swarm Optimization for Multi-Objective Problems
A Novel Hybrid Particle Swarm Optimization for Multi-Objective Problems
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DOI：
10.1007/978-3-642-05253-8_4
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
Siwei Jiang;Z. Cai
中科院分区：
文献类型：
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作者：
Siwei Jiang;Z. Cai
针对多目标优化问题，提出了一种新的混合粒子群优化算法（HPSODE）。新算法主要包括三个方面的改进：（1）种群初始化采用统计方法均匀设计;（2）再生方法分为两个阶段：第一阶段采用自适应粒子群模型更新粒子，并引入收缩因子χ;第二阶段采用差分进化算子，并引入存档;（3）设计了一种新的接受规则距离/体积适应度来更新存档。在jMetal2.1环境下对ZDTx和DTLZx问题进行了实验，结果表明，新算法在加性Epsilon、HyperVolume、遗传距离、逆遗传距离等方面均优于OMOPSO、SMPSO。
To solve the multi-objective problems, a novel hybrid particle swarm optimization algorithm is proposed(called HPSODE). The new algorithm includes three major improvement: (I)Population initialization is constructed by statistical methodUniform Design, (II)Regeneration method has two phases: the first phase is particles updated by adaptive PSO model with constriction factorχ, the second phase is Differential Evolution operator with archive, (III)A new accept rule calledDistance/volume fitnessis designed to update archive. Experiment on ZDTx and DTLZx problems by jMetal 2.1, the results show that the new hybrid algorithm significant outperforms OMOPSO, SMPSO in terms of additive Epsilon, HyperVolume, Genetic Distance, Inverted Genetic Distance.