D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces
D2MOPSO: MOPSO Based on Decomposition and Dominance with Archiving Using Crowding Distance in Objective and Solution Spaces
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DOI：
10.1162/evco_a_00104
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发表时间：
2014-03-01
影响因子：
6.8
通讯作者：
McCall, J.
中科院分区：
文献类型：
--
作者：
Al Moubayed, N.;Petrovski, A.;McCall, J.
本文改进了最近开发的多目标粒子群优化算法，结合优势与分解用于多目标优化的上下文中。分解通过将多目标问题（MOP）转化为一组聚合问题来简化它，而优势在建立领导者档案中起着重要作用。引入了一种新的归档技术，有助于在目标和解决方案空间中实现更好的多样性和覆盖范围。标准的基准，包括约束和无约束的测试问题，通过比较它与三个国家的最先进的多目标进化算法：MOEA/D，OMOPSO和dMOPSO的改进方法进行评估。实验结果的比较和分析表明，该算法具有较强的竞争力和高效性，适用于多种多目标优化问题。
This paper improves a recently developed multi-objective particle swarm optimizer that incorporates dominance with decomposition used in the context of multi-objective optimization. Decomposition simplifies a multi-objective problem (MOP) by transforming it to a set of aggregation problems, whereas dominance plays a major role in building the leaders' archive. introduces a new archiving technique that facilitates attaining better diversity and coverage in both objective and solution spaces. The improved method is evaluated on standard benchmarks including both constrained and unconstrained test problems, by comparing it with three state of the art multi-objective evolutionary algorithms: MOEA/D, OMOPSO, and dMOPSO. The comparison and analysis of the experimental results, supported by statistical tests, indicate that the proposed algorithm is highly competitive, efficient, and applicable to a wide range of multi-objective optimization problems.