An Opposition-Based Modified Differential Evolution Algorithm for Numerical Optimization Problems

Abstract:

In this paper, a new opposition-based modified differential evolution algorithm (OMDE) is proposed. This algorithm integrates the opposed-learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark functions tested, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

• Periodical: Applied Mechanics and Materials (Volume 415)
• Pages: 309-313
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.415.309
• Online since: September 2013
• Authors: Hong Gang Xia, Qing Zhou Wang
• Keywords: Convergence, OMDE, Opposed-Learning, Stability, Strategy

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