Differential Evolution with wavelet basis function based parameter control and dimensional interchange for diversity enhancement

Abstract

Differential Evolution (DE) is a potent population-based global optimization algorithm which has already proven efficient for optimization demands in engineering applications. However, even the state-of-the-art DE variants still suffer premature convergence and lack of diversity. In order to overcome the above mentioned weaknesses, this paper proposes a brand-new DE variant, namely zDE algorithm, for single-objective numerical optimization. The main contributions can be summarized as follows: First, an improved wavelet basis function is incorporated into the generation of the scale factor F and a Minkowski Distance based adaptation scheme is proposed for the adaptation of it. Second, a new trial vector generation strategy is first proposed as a supplementary of the existing strategies, and t-distribution is incorporated into this strategy. Third, a novel diversity enhancement technique is firstly proposed by changing the dimensional parameters of the individuals in the population. The zDE algorithm is validated under 88 benchmarks from the CEC2013, CEC2014, and CEC2017 test suites for real-parameter single-objective optimization, and the results show the superiority of our algorithm in comparison with the recent state-of-the-art DE variants.