Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem
Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem
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DOI：
10.1007/s10845-013-0814-2
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发表时间：
2013-07
影响因子：
8.3
通讯作者：
Wenqiang Zhang;M. Gen;J. Jo
中科院分区：
文献类型：
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作者：
Wenqiang Zhang;M. Gen;J. Jo
工艺规划与调度（PPS）是制造系统中一个重要而又棘手的问题。许多研究使用多目标进化算法（MOEA）来解决这类问题，但它们在质量和计算速度上都不能达到令人满意的结果。提出了一种基于混合采样策略的多目标进化算法（HSS-MOEA）来求解PPS问题。HSS-MOEA巧妙地结合了矢量评估遗传算法（VEGA）的优点和根据新的帕累托支配和支配关系的适应度函数（PDDR-FF）的采样策略。VEGA的采样策略更倾向于帕累托前沿的边缘区域，而基于PDDR-FF的采样策略有向帕累托前沿中心区域收敛的趋势。这两种机制保持了收敛速度和分布性能。数值比较表明，HSS-MOEA算法在效率（收敛性和分布性）方面优于基于广义Pareto的尺度无关适应度函数的遗传算法和VEGA，而效率相当.此外，HSS-MOEA的效能表现也优于NSGA-Ⅱ和SPEA 2，效率明显优于它们的性能。
Process planning and scheduling (PPS) is an important and practical topic but very intractable problem in manufacturing systems. Many research studies used multiobjective evolutionary algorithm (MOEA) to solve such problems; however, they cannot achieve satisfactory results in both quality and computational speed. This paper proposes a hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MOEA) to deal with the PPS problem. HSS-MOEA tactfully combines the advantages of vector evaluated genetic algorithm (VEGA) and a sampling strategy according to a new Pareto dominating and dominated relationship-based fitness function (PDDR-FF). The sampling strategy of VEGA prefers the edge region of the Pareto front and PDDR-FF-based sampling strategy has the tendency converging toward the central area of the Pareto front. These two mechanisms preserve both the convergence rate and the distribution performance. The numerical comparisons state that the HSS-MOEA is better than a generalized Pareto-based scale-independent fitness function based genetic algorithm combing with VEGA in efficacy (convergence and distribution) performance, while the efficiency is closely equivalent. Moreover, the efficacy performance of HSS-MOEA is also better than NSGA-II and SPEA2, and the efficiency is obviously better than their performance.