Differential evolution with improved individual-based parameter setting and selection strategy

In this paper, a novel differential evolution (DE) algorithm is proposed to improve the search efficiency of DE by employing the information of individuals to adaptively set the parameters of DE and update population. Firstly, a combined mutation strategy is developed by using two mixed mutation strategies with a prescribed probability. Secondly, the fitness values of original and guiding individuals are used to guide the parameter setting. Finally, a diversity-based selection strategy is designed by assembling greedy selection strategy and defining a new weighted fitness value based on the fitness values and positions of target and trial individuals. The proposed algorithm compares with eight existing algorithms on CEC 2005 and 2014 contest test instances, and is applied to solve the Spread Spectrum Radar Polly Code Design. Experimental results show that the proposed algorithm is very competitive. (C) 2017 Published by Elsevier B.V.