Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization

TL;DR

This paper introduces an adaptive resonance theory-based clustering method for many-objective optimization, enhancing reference vector adaptation and mating selection to improve performance on complex problems.

Contribution

It proposes a novel adaptive resonance theory-based clustering approach that effectively utilizes topological structure information for reference vector adaptation and mating selection in MOEAs.

Findings

Outperforms 8 state-of-the-art MOEAs on 78 test problems.

Shows superior optimization on MaOPs with irregular Pareto fronts.

Demonstrates the effectiveness of topological structure utilization.

Abstract

Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs). Especially, algorithms that employ a clustering algorithm with a topological structure (i.e., a network composed of nodes and edges) show superior optimization performance to other MOEAs for MaOPs with irregular Pareto optimal fronts (PFs). These algorithms, however, do not effectively utilize information of the topological structure in the search process. Moreover, the clustering algorithms typically used in conventional studies have limited clustering performance, inhibiting the ability to extract useful information for the search process. This paper proposes an adaptive reference vector-guided evolutionary algorithm using an adaptive resonance theory-based clustering with a topological structure. The proposed algorithm utilizes the information of the topological structure not only for reference vector adaptation but also for mating selection. The proposed algorithm is compared with 8 state-of-the-art MOEAs on 78 test problems. Experimental results reveal the outstanding optimization performance of the proposed algorithm over the others on MaOPs with various properties.