Accelerating differential evolution based on a subset-to-subset survivor selection operator
Differential evolution (DE) is one of the most powerful and effective evolutionary algorithms for solving global optimization problems. However, just like all other metaheuristics, DE also has some drawbacks, such as slow and/or premature convergence. This paper proposes a new subset-to-subset selection operator to improve the convergence performance of DE by randomly dividing target and trial populations into several subsets and employing the ranking-based selection operator among corresponding subsets. The proposed framework gives more survival opportunities to trial vectors with better objective function values. Experimental results show that the proposed method significantly improves the performance of the original DE algorithm and several state-of-the-art DE variants on a series of benchmark functions.
The file attached to this record is the author's final peer reviewed version.
Citation : Guo, J., Li, Z. and Yang, S. (2018) Accelerating differential evolution based on a subset-to-subset survivor selection operator. Soft Computing,
ISSN : 1432-7643
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes