Multiple parents guided differential evolution for large scale optimization
Qiang Yang, Han-Yu Xie, Wei–Neng Chen, Jun Zhang · 2016
Large scale optimization has become an important and challenging area in evolutionary computation. To solve this kind of problems efficiently, this paper proposes a multiple parents guided differential evolution (MPGDE) algorithm. Instead of using only one parent to guide each individual in traditional DE variants, multiple top ranked parents are utilized to direct each individual to search the space. Since the failed parents or trial vectors may also contain useful information, we maintain an archive to preserve these failed individuals and utilize a niching method to update the archive during evolution. Combining the above together, we put forward a new mutation strategy for DE. Cooperated with existing self-adaptive strategies for parameters in DE, MPGDE can afford a good balance between exploration and exploitation, so that promising performance can be obtained. Extensive experiments are conducted on 20 CEC'2010 large scale benchmark functions with 1000 dimensions to verify the efficacy and effectiveness of the developed MPGDE in comparison with several state-of-the-art algorithms dealing with large scale problems.