Surrogate-Assisted Multi-Tasking Memetic Algorithm

This paper proposes a surrogate-assisted multitasking memetic algorithm (SaM-MA) for multi-tasking optimization. In the proposed SaM-MA, the population is divided into multiple sub-populations, with each sub-population focusing on solving one task. Each sub-population is evolved by three components. The first is the global search component which used differential evolutionary algorithm to search for the global optimal solution for the corresponding task. The second component is a surrogate model with Gaussian process, which is used to predict the best solution, so as to reduce the number of fitness evaluations and to improve the search efficiency. The third component is the local search component which utilizes the CMA-ES to locally exploiting the neighboring regions of promising solutions. In addition, the crossover operators in the global search component are extended so as to facilitate knowledge transfer between sub-populations, The proposed SaM-MA is tested on nine benchmark multi-tasking optimization problems in the CEC2017 competition. The experiment results have demonstrated the efficacy of the proposed SaM-MA in terms of solution accuracy and search efficiency.