Multiobjective Multitasking Optimization Based on Decomposition with Dual Neighborhoods

TL;DR

This paper introduces a novel multiobjective multitasking evolutionary algorithm that leverages dual neighborhoods and gray relation analysis to improve search efficiency across multiple tasks.

Contribution

It proposes a decomposition-based algorithm with dual neighborhoods, effectively exploiting relationships among subproblems of different tasks for enhanced optimization performance.

Findings

Outperforms four state-of-the-art algorithms on test problems

Utilizes gray relation analysis for inter-task neighborhood definition

Demonstrates improved convergence and diversity in solutions

Abstract

This paper proposes a multiobjective multitasking optimization evolutionary algorithm based on decomposition with dual neighborhood. In our proposed algorithm, each subproblem not only maintains a neighborhood based on the Euclidean distance among weight vectors within its own task, but also keeps a neighborhood with subproblems of other tasks. Gray relation analysis is used to define neighborhood among subproblems of different tasks. In such a way, relationship among different subproblems can be effectively exploited to guide the search. Experimental results show that our proposed algorithm outperforms four state-of-the-art multiobjective multitasking evolutionary algorithms and a traditional decomposition-based multiobjective evolutionary algorithm on a set of test problems.