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Simpler and Enhanced Multifactorial Evolutionary Algorithms for Continuous Optimization Tasks

Multifactorial optimization has become one of the most promising paradigms for evolutionary multitasking within the field of computational intelligence. This methodology can improve the performance results for multiple, simultaneous optimization problems by exploiting the transfer of genetic information between them. In this paper, we present an indepth analysis of this approach by considering several variations of the standard multifactorial evolutionary algorithm (MFEA). By using a simpler structure together with some enhanced operators, two new multifactorial evolutionary algorithms are proposed. We demonstrate that, compared with the traditional MFEA, our approach produces better results on a set of continuous optimization benchmark problems.