A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems

This paper presents a self-adaptive hybrid variant of differential evolution (DE) algorithm and naked mole-rat algorithm (NMRA), namely SaDN. The algorithm is altogether a new version, designed to overcome the local optima stagnation and poor exploration properties of DE and NMRA respectively. The new algorithm has been designed by incorporating DE into the worker phase of NMRA while keeping all the major parameters of both the algorithms intact. In order to make the algorithm self-adaptive, seven different mutation strategies have been explored for different parameters, and it was found that Levy based scaling factor and sigmoidal mating factor are the best parameters. Apart from these parameters, adaptive properties have been introduced to all other parameters so that no user-based initialization of parameters is required. For performance evaluation, the proposed SaDN is tested on CEC 2005, CEC 2014 and CEC 2019 benchmark problems and comparison is performed for variable population size and higher dimension sizes. From the experimental results, it has been found that the proposed SaDN performs better with respect to other major state-of-the-art algorithms from the literature. Apart from that, SaDN is subjected to three engineering design problems and compared with other algorithms. Numerical results demonstrate that SaDN shows better performance and is statistically significant in terms of Wilcoxon's rank-sum test, Freidman's test and computational complexity. The source code for the proposed algorithms is available at: https://github.com/rohitsalgotra. (C) 2021 Elsevier B.V. All rights reserved.