Al Moubayed N, Petrovski A, McCall J
Robert Gordon University, Aberdeen, AB25 1HG, UK
Evol Comput. 2014 Spring;22(1):47-77. doi: 10.1162/EVCO_a_00104. Epub 2013 Oct 30.
This paper improves a recently developed multi-objective particle swarm optimizer (D2MOPSO) 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. D2MOPSO 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.
本文改进了一种最近开发的多目标粒子群优化器（D2MOPSO），该优化器将支配关系与用于多目标优化的分解方法相结合。分解通过将多目标问题（MOP）转化为一组聚合问题来简化该问题，而支配关系在构建领导者存档中起着主要作用。D2MOPSO引入了一种新的存档技术，有助于在目标空间和解决方案空间中实现更好的多样性和覆盖范围。通过将改进后的方法与三种先进的多目标进化算法：MOEA/D、OMOPSO和dMOPSO进行比较，在包括约束和无约束测试问题在内的标准基准上对其进行了评估。实验结果的比较和分析，在统计测试支持下，表明所提出的算法具有高度竞争力、高效且适用于广泛的多目标优化问题。
