Two-Stage Double Niched Evolution Strategy for Multimodal Multiobjective Optimization

doi:10.1109/TEVC.2021.3064508

Two-Stage Double Niched Evolution Strategy for Multimodal Multiobjective Optimization

2021-08-01

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DOI：10.1109/TEVC.2021.3064508

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In recent years, numerous efficient and effective multimodal multiobjective evolutionary algorithms (MMOEAs) have been developed to search for multiple equivalent sets of Pareto optimal solutions simultaneously. However, some of the MMOEAs prefer convergent individuals over diversified individuals to construct the mating pool, and the individuals with slightly better decision space distribution may be replaced by significantly better objective space distribution. Therefore, the diversity in the decision space may become deteriorated, in spite of the decision and objective diversities have been taken into account simultaneously in most MMOEAs. Because the Pareto optimal subsets may have various shapes and locations in the decision space, it is very difficult to drive the individuals converged to every Pareto subregion with a uniform density. Some of the Pareto subregions may be overly crowded, while others are rather sparsely distributed. Consequently, many existing MMOEAs obtain Pareto subregions with imbalanced density. In this article, we present a two-stage double niched evolution strategy, namely DN-MMOES, to search for the equivalent global Pareto optimal solutions which can address the above challenges effectively and efficiently. The proposed DN-MMOES solves the multimodal multiobjective optimization problem (MMOP) in two stages. The first stage adopts the niching strategy in the decision space, while the second stage adapts double niching strategy in both spaces. Moreover, an effective decision density self-adaptive strategy is designed for improving the imbalanced decision space density. The proposed algorithm is compared against eight state-of-the-art MMOEAs. The inverted generational distance union (IGDunion) performance indicator is proposed to fairly compare two competing MMOEAs as a whole. The experimental results show that DN-MMOES provides a better performance to search for the complete Pareto Subsets and Pareto Front on IDMP and CEC 2019 MMOPs test suite.

Keywords:

Pareto optimization

Optimization

Evolutionary computation

Spatial diversity

Search problems

Object recognition

Statistics

Evolution strategy

multimodal multiobjective evolutionary algorithm (MMOEA)

multimodal multiobjective optimization problem (MMOP)

Two-Stage Double Niched Evolution Strategy for Multimodal Multiobjective Optimization

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