Evolutionary Multitasking for Optimization Based on Generative Strategies

Abstract

Evolutionary multitasking (EMT) is one of the emerging topics in evolutionary computation. EMT can solve multiple related optimization tasks simultaneously and enhance the optimization of each task via knowledge sharing among tasks. Many EMT algorithms have been proposed and achieved success in various problems, yet EMT for multi-objective optimization remains a big challenge. The existing multi-objective EMT algorithms tend to suffer from slow convergence and difficulty in generating high-quality knowledge. To alleviate these issues, this paper proposes a new EMT algorithm namely EMT-GS for multi-objective optimization based on two generative strategies. Particularly, generative adversarial networks and inertial differential evolution are introduced to generate transferable knowledge and offspring, respectively. A generative adversarial network is trained periodically for each source-target task pair, based on which helpful knowledge is generated from the source task and transferred to boost the solving of the target task. To accelerate the population convergence, the inertial differential evolution strategy is put forward to generate offspring in a promising direction according to the individuals from the previous generation and the transferred knowledge. The performance of EMT-GS is validated on three multitasking multi-objective benchmark problems. The experimental results highlight the excellent competitiveness of EMT-GS compared to other state-of-the-art multi-objective EMT algorithms.