A Multiobjective State Transition Algorithm Based on Decomposition

TL;DR

This paper introduces a novel multi-objective optimization algorithm that enhances the Tchebycheff aggregation function by incorporating a matching degree concept, improving convergence and diversity in evolutionary algorithms.

Contribution

It proposes a new decomposition method with a matching degree-based Tchebycheff function and a multi-objective state transition algorithm, advancing multi-objective optimization techniques.

Findings

The proposed algorithm outperforms existing methods in benchmark tests.

Incorporating matching degree improves convergence and diversity.

Experimental results demonstrate high competitiveness of the new algorithm.

Abstract

Aggregation functions largely determine the convergence and diversity performance of multi-objective evolutionary algorithms in decomposition methods. Nevertheless, the traditional Tchebycheff function does not consider the matching relationship between the weight vectors and candidate solutions. In this paper, the concept of matching degree is proposed which employs vectorial angles between weight vectors and candidate solutions. Based on the matching degree, a new modified Tchebycheff aggregation function is proposed, which integrates matching degree into the Tchebycheff aggregation function. Moreover, the proposed decomposition method has the same functionality with the Tchebycheff aggregation function. Based on the proposed decomposition approach, a new multiobjective optimization algorithm named decomposition-based multi-objective state transition algorithm is proposed. Relevant experimental results show that the proposed algorithm is highly competitive in comparison with other state-of-the-art multiobjetive optimization algorithms.