Enhancing MOEA with component-emphasizing mechanism for multi-objective optimization

Song Yang, Ji Junzhong, Liu Chunnian
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Abstract

Multi-objective optimization is an important and challenging topic in the field of industrial design and scientific research because real-world problems usually involve several conflicting objectives. Since a multi-objective evolutionary algorithms (MOEA) is able to obtain an approximation to the Pareto optimal set and provide substantial information of the tradeoff between objectives, it is becoming one of the most successful methods for multi-objective optimization. Usually, an MOEA generates new trial solutions (offspring) with some candidate decision vectors (parents) to search for the promising areas and make the population evolve towards the Pareto optimal set. Moreover, in the reproduction procedure, most of MOEAs view the decision vector as a whole, and do not recognize the effects of a single component on the new trial solutions. In this paper, we propose the component-emphasizing mechanism for enhancing the search ability of MOEAs. In this mechanism, each component of a decision vector is viewed as an independent factor affecting the quality of the solution. Based on the mechanism, a new MOEA is presented. Finally, the performance of this new algorithm is compared with two other promising MOEAs, namely, NSGA-II and GDE3, on a set of test instances. The experimental results have shown that the proposed algorithm outperforms the others in solution quality and time cost.
用多目标优化的构件强调机制增强MOEA
多目标优化是工业设计和科学研究领域的一个重要而富有挑战性的课题,因为现实世界的问题通常涉及几个相互冲突的目标。由于多目标进化算法(MOEA)能够逼近Pareto最优集,并提供目标间权衡的大量信息,因此成为最成功的多目标优化方法之一。通常,MOEA生成新的尝试解(子代)和一些候选决策向量(亲代)来搜索有希望的区域,并使种群向Pareto最优集进化。此外,在复制过程中,大多数moea将决策向量视为一个整体,而不承认单个组件对新试验解决方案的影响。在本文中,我们提出了一种构件强调机制来增强moea的搜索能力。在这种机制中,决策向量的每个组成部分被视为影响解决方案质量的独立因素。在此基础上,提出了一种新的MOEA。最后,在一组测试实例上,将该算法与NSGA-II和GDE3这两种有前景的moea进行性能比较。实验结果表明,该算法在求解质量和时间成本方面优于其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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