Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem

TL;DR

This paper introduces MTEA-AST, an adaptive evolutionary multitasking algorithm that effectively transfers knowledge across multiple combinatorial optimization problems, addressing challenges like cross-domain transfer and negative transfer.

Contribution

The paper proposes a novel adaptive seed transfer method with dimension unification and task similarity strategies for multitasking evolutionary algorithms.

Findings

MTEA-AST outperforms state-of-the-art EMTOs in various combinatorial problems.

The adaptive transfer strategy effectively reduces negative transfer.

The method is applicable in both same-domain and cross-domain multitasking environments.

Abstract

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs.