A novel mutation operator based on the union of fitness and design spaces information for Differential Evolution

作者：Sharifi Noghabi Hossein; Mashhadi Habib Rajabi^*; Shojaee Kambiz

来源：Soft Computing, 2017, 21(22): 6555-6562.

DOI：10.1007/s00500-016-2359-8

• Differential Evolution
• Union Differential Evolution
• Intelligent selection
• Ranking-based mutation
• Proximity-based mutation

Differential Evolution (DE) is one of the most successful and powerful evolutionary algorithms for global optimization problem. The most important operator in this algorithm is mutation operator, in which parents are selected randomly to participate in it. Recently, numerous papers are tried to make this operator more intelligent by selection of parents for mutation intelligently. The intelligent selection for mutation vectors is performed by applying design space (also known as decision space) criterion or fitness space criterion; however, in both cases, half of valuable information of the problem space is disregarded. In this article, a Union Differential Evolution (UDE) is proposed which takes advantage of both design and fitness spaces criteria for intelligent selection of mutation vectors. The experimental analysis on UDE are performed on CEC2005 benchmarks and the results stated that UDE significantly improved the performance of DE in comparison with other methods that only use one criterion for intelligent selection.

• 出版日期2017-11