期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 22, 期 4, 页码 578-594出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2017.2743016
关键词
Exemplar selection; high-dimensional problems; large-scale optimization; level-based learning swarm optimizer (LLSO); particle swarm optimization (PSO)
资金
- National Natural Science Foundation of China [61622206, 61379061, 61332002]
- Natural Science Foundation of Guangdong [2015A030306024]
- Guangdong Special Support Program [2014TQ01X550]
- Guangzhou Pearl River New Star of Science and Technology [201506010002]
In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach them in accordance with their cognitive and learning abilities. Inspired from this idea, we consider particles in the swarm as mixed-level students and propose a level-based learning swarm optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging in evolutionary computation. At first, a level-based learning strategy is introduced, which separates particles into a number of levels according to their fitness values and treats particles in different levels differently. Then, a new exemplar selection strategy is designed to randomly select two predominant particles from two different higher levels in the current swarm to guide the learning of particles. The cooperation between these two strategies could afford great diversity enhancement for the optimizer. Further, the exploration and exploitation abilities of the optimizer are analyzed both theoretically and empirically in comparison with two popular particle swarm optimizers. Extensive comparisons with several state-of-the-art algorithms on two widely used sets of large-scale benchmark functions confirm the competitive performance of the proposed optimizer in both solution quality and computational efficiency. Finally, comparison experiments on problems with dimensionality increasing from 200 to 2000 further substantiate the good scalability of the developed optimizer.
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