A multi-objective particle swarm optimizer based on decomposition
A multi-objective particle swarm optimizer based on decomposition
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DOI：
10.1145/2001576.2001587
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Saúl Zapotecas Martínez;C. Coello
中科院分区：
文献类型：
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作者：
Saúl Zapotecas Martínez;C. Coello
粒子群优化（PSO）算法的简单性和成功性，促使研究人员将这些技术的使用扩展到多目标优化领域。针对连续无约束多目标优化问题，提出了一种基于分解的多目标粒子群优化算法（MOPSO）。提出的基于分解的多目标粒子群优化算法（dMOPSO），更新的位置，每个粒子使用一组解决方案被认为是全球最好的分解方法。dMOPSO的主要特点是使用一个内存重新初始化过程，旨在提供多样性的群体。我们提出的方法进行了比较，相对于两个基于分解的多目标进化算法（MOEAs），这是代表国家的最先进的领域。我们的研究结果表明，我们提出的方法是有竞争力的，它优于两个MOEA，它是比较在大多数测试问题中采用。
The simplicity and success of particle swarm optimization (PSO) algorithms, has motivated researchers to extend the use of these techniques to the multi-objective optimization field. This paper presents a multi-objective particle swarm optimization (MOPSO) algorithm based on a decomposition approach, which is intended for solving continuous and unconstrained multi-objective optimization problems (MOPs). The proposed decomposition-based multi-objective particle swarm optimizer (dMOPSO), updates the position of each particle using a set of solutions considered as the global best according to the decomposition approach. dMOPSO is mainly characterized by the use of a memory reinitialization process which aims to provide diversity to the swarm. Our proposed approach is compared with respect to two decomposition-based multi-objective evolutionary algorithms (MOEAs) which are representative of the state-of-the-art in the area. Our results indicate that our proposed approach is competitive and it outperforms the two MOEAs with respect to which it was compared in most of the test problems adopted.