A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm

To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measu...

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Vydáno v:IEEE transactions on cybernetics Ročník 45; číslo 10; s. 2202 - 2213
Hlavní autoři: Siwei Jiang, Jie Zhang, Yew-Soon Ong, Zhang, Allan N., Puay Siew Tan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Evolutionary computation
Genetic algorithms
Hypervolume (HV)
indicator-based
jMetal
Joints
Laboratories
multiobjective evolutionary algorithms (MOEAs)
Optimization algorithms
Pareto dominance-based
scalarizing function-based
Sociology
Statistics
Time complexity
Vectors
scalarizing function-based
jMetal
multiobjective evolutionary algorithms (MOEAs)
indicator-based
Hypervolume (HV)
Pareto dominance-based
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:To find diversified solutions converging to true Pareto fronts (PFs), hypervolume (HV) indicator-based algorithms have been established as effective approaches in multiobjective evolutionary algorithms (MOEAs). However, the bottleneck of HV indicator-based MOEAs is the high time complexity for measuring the exact HV contributions of different solutions. To cope with this problem, in this paper, a simple and fast hypervolume indicator-based MOEA (FV-MOEA) is proposed to quickly update the exact HV contributions of different solutions. The core idea of FV-MOEA is that the HV contribution of a solution is only associated with partial solutions rather than the whole solution set. Thus, the time cost of FV-MOEA can be greatly reduced by deleting irrelevant solutions. Experimental studies on 44 benchmark multiobjective optimization problems with 2-5 objectives in platform jMetal demonstrate that FV-MOEA not only reports higher hypervolumes than the five classical MOEAs (nondominated sorting genetic algorithm II (NSGAII), strength Pareto evolutionary algorithm 2 (SPEA2), multiobjective evolutionary algorithm based on decomposition (MOEA/D), indicator-based evolutionary algorithm, and S-metric selection based evolutionary multiobjective optimization algorithm (SMS-EMOA)), but also obtains significant speedup compared to other HV indicator-based MOEAs.
Bibliografie:ObjectType-Article-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2014.2367526