Differential Evolution with fitness-difference based parameter control and hypervolume diversity indicator for numerical optimization.

Differential Evolution (DE) is one of the most popular and powerful branches of evolutionary algorithm family. However, even many state-of-the-art DE-based variants still exist weakness such as improper parameter adaptation and population stagnation during the later stage of evolution. To mitigate these deficiencies, differential evolution with fitness-difference based parameter control and hypervolume diversity indicator (FDHD-DE) is proposed in this paper. Firstly, a semi-adaptive adaptation scheme for control parameters is proposed, in which the generation of scale factor and crossover rate is modified by dividing into two stages, thus enhancing the efficiency of parameter adaptation. Secondly, a novel fitness-based weighting strategy is proposed to improve the performance of existing success history-based adaptation by employing a novel approach of utilizing fitness information. Finally, a hypervolume-based diversity indicator and corresponding dimension exchange strategy are proposed to alleviate the problem of population stagnation. The performance of FDHD-DE is verified on the 88 benchmark functions from Congress on Evolutionary Computation (CEC) 2013, CEC 2014, and CEC 2017 test suites on 10D, 30D and 50D and a real-world application. The experiment results are compared with several state-of-art DE variants, and the results show that FDHD-DE has better performance, both in terms of solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]