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A Two-Phase Learning-Based Swarm Optimizer for Large-Scale Optimization
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DOI：10.1109/TCYB.2020.2968400.png)
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Abstract
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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.
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
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