Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives

TL;DR

This paper introduces KTDMOEA, a novel algorithm that improves diversity and convergence in dynamic multi-objective optimization problems with changing objectives by effectively transferring knowledge and adapting the Pareto set.

Contribution

The paper proposes KTDMOEA, a new knowledge transfer algorithm that dynamically adjusts the Pareto set to maintain diversity in changing objective environments.

Findings

KTDMOEA outperforms existing algorithms in diversity and convergence.

It effectively adapts to fast-changing environments.

Experimental results on 13 benchmarks validate its superiority.

Abstract

Different from most other dynamic multi-objective optimization problems (DMOPs), DMOPs with a changing number of objectives usually result in expansion or contraction of the Pareto front or Pareto set manifold. Knowledge transfer has been used for solving DMOPs, since it can transfer useful information from solving one problem instance to solve another related problem instance. However, we show that the state-of-the-art transfer algorithm for DMOPs with a changing number of objectives lacks sufficient diversity when the fitness landscape and Pareto front shape present nonseparability, deceptiveness or other challenging features. Therefore, we propose a knowledge transfer dynamic multi-objective evolutionary algorithm (KTDMOEA) to enhance population diversity after changes by expanding/contracting the Pareto set in response to an increase/decrease in the number of objectives. This enables a solution set with good convergence and diversity to be obtained after optimization. Comprehensive studies using 13 DMOP benchmarks with a changing number of objectives demonstrate that our proposed KTDMOEA is successful in enhancing population diversity compared to state-of-the-art algorithms, improving optimization especially in fast changing environments.