Modified Differential Evolution Algorithm for Numerical Optimization Problems

Abstract

In this paper, a modified differential evolution algorithm (MDE) developed to solve unconstrained numerical optimization problems. The MDE algorithm employed random position updating and disturbance operation to replaces the traditional mutation operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the MDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

A Modified Differential Evolution Algorithm Based on Improving A New Mutation Strategy and Self-Adaptation Crossover

“…Xia & Wang [7] proposed a novel Self adaptive Differential Evolution algorithm (SaDE), where the two control parameters of F and CR in addition to the choice of learning strategy are not required to be pre-specified. Tanabe & Fukunaga [8] proposed L-SHADE that extended the SHADE with Linear Population Size Reduction (LPSR).…”