An Improved Differential Evolution Algorithm for Numerical Optimization Problems

Abstract:

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover 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 scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

• Periodical: Applied Mechanics and Materials (Volume 415)
• Pages: 349-352
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.415.349
• Online since: September 2013
• Authors: Hong Wei Zhao, Hong Gang Xia
• Keywords: Convergence, DE, Dynamic, IDE, Mutation Operation

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