TL-MOMFEA: a transfer learning-based multi-objective multitasking optimization evolutionary algorithm

Abstract

Evolutionary multi-objective multitasking optimization (MTO) has emerged as a popular research field in evolutionary computation. By simultaneously considering multiple objectives and tasks while identifying valuable knowledge for intertask transfer, MTO aims to discover solutions that deliver optimal performance across all objectives and tasks. Nevertheless, MTO presents a substantial challenge concerning the effective transport of high-quality information between tasks. To handle this challenge, this paper introduces a novel approach named TL-MOMFEA (multi-objective multifactorial evolutionary algorithm based on domain transfer learning) for MTO problems. TL-MOMFEA uses domain-transfer learning to adapt the population from one task to another, resulting in the reproduction of higher-quality solutions. Furthermore, TL-MOMFEA employs a model transfer strategy where population distribution rules learned from one task are succinctly summarized and applied to similar tasks. By capitalizing on the knowledge acquired from solved tasks, TL-MOMFEA effectively circumvents futile searches and accurately identifies global optimum predictions with increased precision. The effectiveness of TL-MOMFEA is evaluated through experimental studies in two widely used test suites, and experimental comparisons have shown that the proposed paradigm achieves excellent results in terms of solution quality and search efficiency, thus demonstrating its clear superiority over other state-of-the-art MTO frameworks.