Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design

Differential evolution (DE) is one of the best evolutionary algorithms (EAs). In recent decades, many techniques have been developed to enhance the performance of this algorithm, such as the Improve Self-Adaptive Differential Evolution (ISADE) algorithm. Based on the analysis of the aspects that may improve the performance of ISADE, we proposed a modified ISADE version with applying the Gauss distribution for mutation procedure. In ISADE, to determine the scaling factor (F), the population is ranked, then, based on the rank number, population size, and current generation, the formula of the Sigmoid function is used. In the proposed algorithm, F is amplified by a factor which is generated based on Gaussian distribution. It has the potential to enhance the variety of population. In comparison with several reference algorithms regarding converging speed and the consistency of optimal solutions, the simulation results reveal the performance of the suggested algorithm is exceptional.

Automated summary

In this study, a modified version of the ISADE algorithm is proposed, which applies the Gauss distribution for the mutation procedure to enhance the diversity of the population. Simulation results show that the suggested algorithm performs exceptionally well compared to other reference algorithms in terms of converging speed and consistency of optimal solutions.