New adaption based mutation operator on differential evolution algorithm
Shailendra Pratap Singh · Intelligent Decision Technologies · 2018
Among meta-heuristics algorithms, differential evolution (DE) is powerful nature-inspired algorithm used to solve the nonlinear problems. However, at higher generations there is an increase in the computational cost. In this paper, a new approach has been proposed from a new adaption based mutation operator in which the variations of a particular element is kept constant. In the same way, to keep an element constant called as “diversity” in DE, new adaption based mutation operator has been incorporated. The proposed variants are proliferated new adaption based operator with one more vector, named as new adaption based mutation operator in the existing mutation vector to provide more diversity for selecting effective mutant solutions. The proposed approach provides more promising solutions to explorer the evolution and helps DE evade the circumstance of stagnation. Comparisons with other DE variants such as CPI-DE, TSDE, ToPDE, MPEDE and JADEcr establishes that the proposed new adaption based mutation operator is able to improve the performance of DE.