Many real-world optimization problems such as engineering design can be eventually modeled as the corresponding multiobjective optimization problems (MOPs) which must be solved to obtain approximate Pareto optimal fronts. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been regarded as a significantly promising approach for solving MOPs. Recent studies have shown that MOEA/D with uniform weight vectors is well-suited to MOPs with regular Pareto optimal fronts, but its performance in terms of diversity usually deteriorates when solving MOPs with irregular Pareto optimal fronts. In this way, the solution set obtained by the algorithm can not provide more reasonable choices for decision makers. In order to efficiently overcome this drawback, we propose an improved MOEA/D algorithm by virtue of the well-known Pascoletti-Serafini scalarization method and a new strategy of multi-reference points. Specifically, this strategy consists of the setting and adaptation of reference points generated by the techniques of equidistant partition and projection. For performance assessment, the proposed algorithm is compared with existing four state-of-the-art multiobjective evolutionary algorithms on benchmark test problems with various types of Pareto optimal fronts. According to the experimental results, the proposed algorithm exhibits better diversity performance than that of the other compared algorithms. Finally, our algorithm is applied to two real-world MOPs in engineering optimization successfully.
翻译:许多现实世界优化问题,例如工程设计,最终可以模拟成相应的多目标优化问题(MOP),这些问题必须解决,才能找到大致的Pareto最佳战线。基于分解(MOEA/D)的多目标进化算法被认为是解决MOPRS的一个大有希望的方法。最近的研究显示,具有统一重量矢量的MOEA/D完全适合具有常规Pareto最佳战线的MORS,但是,在用不规则的Pareto最佳战线解决MORS时,其多样性方面的表现通常会恶化。这样,算法所设定的解决方案无法为决策者提供更合理的选择。为了有效克服这一退步,我们建议通过众所周知的Pascolortti-Serafini 缩放法和新的多参照点战略改进MOEA/D的算法。具体来说,该战略包括确定和调整由公平偏差和预测技术产生的参考点。关于业绩评估,拟议的算法与现有的四个状态的多目标优化方法为决策者提供更合理的选择。为了有效克服这一退步,我们提出的MEA/D算算算算法,比其他最优的实验性标准的模型,比其他检验方法更成功地地算法。