Evolutionary multitasking for multiobjective optimization based on hybrid differential evolution and multiple search strategy

Abstract

Multitask optimization uses the knowledge transfer between tasks to deal with multiple related tasks simultaneously, which obtains better optimization performance. Currently, most multitask optimization algorithms have shortings such as slow convergence speed, difficulty in generating high-quality solutions and insufficient population diversity. To solve these problems, an new multiobjective multitask evolutionary algorithm is proposed in this paper. The algorithm introduces hybrid differential evolution strategy and multiple search strategy to generate offspring and high-quality solutions. In order to accelerate the population convergence and improve the lack of population diversity, we mixed two differential mutation operators with different functions to generate offspring. While generating high-quality solutions to improve the population convergence speed, some random solutions can also be generated to maintain the population diversity, which enhances the ability of the algorithm to jump out of the local optimal. Secondly, the multiple search strategy uses the information collected from multiple dimensions to optimize decision variables, and generates high-quality solutions by migrating useful knowledge. Finally, the performance of the algorithm is verified on two classical multitask test set. Compared with other related algorithms, the proposed algorithm has a faster convergence speed and better distribution performance, and shows a good ability to jump out of local optimal.