A niching indicator-based multi-modal many-objective optimizer

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 49; S. 134 - 146
Hauptverfasser: Tanabe, Ryoji, Ishibuchi, Hisao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2019
Schlagworte:
Indicator-based evolutionary algorithms
Many-objective optimization
Multi-modal multi-objective optimization
Niching methods
Multi-modal multi-objective optimization
Indicator-based evolutionary algorithms
Niching methods
Many-objective optimization
ISSN:2210-6502
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Zusammenfassung:Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms.
ISSN:2210-6502
DOI:10.1016/j.swevo.2019.06.001