Benchmarks for dynamic multi-objective optimisation
Benchmarks for dynamic multi-objective optimisation
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DOI：
10.1109/cidue.2013.6595776
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Mardé Helbig;A. Engelbrecht
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文献类型：
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作者：
Mardé Helbig;A. Engelbrecht
当算法解决动态多目标优化问题（DMOOP）时，应该使用基准函数来确定算法是否可以克服现实问题中可能出现的特定困难。然而，对于动态多目标优化（DMOO），没有使用标准的基准函数。本文提出了一组理想的DMOO基准函数的特征，以及针对每个特征的建议DMOO。当前DMOOP和动态多目标优化算法（DMOAs）的研究的局限性突出。此外，新的DMOO基准函数与复杂的帕累托最优集（POS）和方法来开发DMOOP与孤立的或欺骗性的帕累托最优的前端（POF）介绍，以解决当前DMOOP确定的局限性。
When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), benchmark functions should be used to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for dynamic multi-objective optimisation (DMOO) there are no standard benchmark functions that are used. This article proposes characteristics of an ideal set of DMOO benchmark functions, as well as suggested DMOOPs for each characteristic. The limitations of current DMOOPs and studies of dynamic multi-objective optimisation algorithms (DMOAs) are highlighted. In addition, new DMOO benchmark functions with complicated Pareto-optimal sets (POSs) and approaches to develop DMOOPs with either an isolated or deceptive Pareto-optimal front (POF) are introduced to address identified limitations of current DMOOPs.