Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 12, Pages 6284-6293Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.2968400
Keywords
Optimization; Convergence; Particle swarm optimization; Cultural differences; Cybernetics; Next generation networking; Random variables; Competitive swarm optimizer (CSO); cooperative coevolution; large-scale optimization; swarm intelligence
Categories
Funding
- National Natural Science Foundation of China [61702129, 61772149, 61866009, U1701267]
- National Key Research and Development Program of China [2018AAA0100305]
- Guangxi Science and Technology Project [2019GXNSFAA245014, AD18281079, AD18216004, 2017GXNFDA198025, AA18118039]
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TPLSO, a two-phase learning-based swarm optimizer, incorporates mass learning and elite learning to achieve effective large-scale optimization. Experimental results demonstrate that TPLSO outperforms several state-of-the-art algorithms in diverse large-scale problems.
In this article, a simple yet effective method, called a two-phase learning-based swarm optimizer (TPLSO), is proposed for large-scale optimization. Inspired by the cooperative learning behavior in human society, mass learning and elite learning are involved in TPLSO. In the mass learning phase, TPLSO randomly selects three particles to form a study group and then adopts a competitive mechanism to update the members of the study group. Then, we sort all of the particles in the swarm and pick out the elite particles that have better fitness values. In the elite learning phase, the elite particles learn from each other to further search for more promising areas. The theoretical analysis of TPLSO exploration and exploitation abilities is performed and compared with several popular particle swarm optimizers. Comparative experiments on two widely used large-scale benchmark datasets demonstrate that the proposed TPLSO achieves better performance on diverse large-scale problems than several state-of-the-art algorithms.
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