Improve Self-Adaptive Control Parameters in Differential Evolution for Solving Constrained Engineering Optimization Problems

Abstract

We proposed a new improvement of self-adaptive strategy for controlling parameters in differential evolution algorithm (ISADE). The differential evolution (DE) algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles (NP), scaling factor (F) and crossover control (CR), which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depend on the characteristics of each objective function, so we have to tune their value in each problem that mean it will take too long time to perform. We present a new version of the DE algorithm for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems and constrained engineering optimization problems.