AREA: Adaptive Reference-set Based Evolutionary Algorithm for Multiobjective Optimisation

TL;DR

This paper introduces AREA, an adaptive reference-set based evolutionary algorithm that dynamically adjusts reference points to improve multiobjective optimization across diverse problem types.

Contribution

The paper proposes a novel framework that adaptively uses references as search targets, enhancing evolutionary algorithms' performance on various multiobjective problems.

Findings

Competitive performance across diverse problems

Robustness demonstrated through extensive sensitivity analysis

Effective use of adaptive references improves search efficiency

Abstract

Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, these algorithms depends largely on problem characteristics, and there is a need to improve their performance for a wider range of problems. References, which are often specified by the decision maker's preference in different forms, are a very effective method to improve the performance of algorithms but have not been fully explored in literature. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state of the arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper.