期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 44, 期 12, 页码 2391-2404出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2014.2307319
关键词
Capacity; convergence; diversity; hypervolume; jMetal; multiobjective optimization (MOO); performance metrics
类别
资金
- A*Star-TSRP
- Singapore Institute of Manufacturing Technology-Nanyang Technological University Joint Laboratory and Collaborative Research Programme on Complex Systems
- Center for Computational Intelligence, NTU
An important consideration of multiobjective optimization (MOO) is the quantitative metrics used for defining the optimality of different solution sets, which is also the basic principle for the design and evaluation of MOO algorithms. Although a plethora of performance metrics have been proposed in the MOO context, there has been a lack of insights on the relationships between metrics. In this paper, we first group the major MOO metrics proposed to date according to four core performance criteria considered in the literature, namely, capacity, convergence, diversity, and convergence-diversity. Then, a comprehensive study is conducted to investigate the relationships among representative group metrics, including generational distance, epsilon-indicator (I-epsilon+(1)), spread (Delta), generalized spread (Delta*), inverted generational distance, and hypervolume. Experimental results indicated that these six metrics show high consistencies when Pareto fronts (PFs) are convex, whereas they show certain contradictions on concave PFs.
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