Abstract
Particles in PSO algorithms evolve only in one group, or in many groups but without interaction between different groups. Inspired by the concept of social class evolution process, a hierarchical competition framework is proposed in this paper. Through the competition mechanism, particles can flow dynamically between different levels, and this can reduce the probability of top-level particles leading to a wrong direction and in this way enhance the global search ability. In this paper, the proposed framework is tested in combination with the canonical PSO and one of the most famous variant particle warm optimizers, named quantum-behaved particle swarm optimizer. All the experiments are run on the CEC’2013 benchmark function database, and the results show that the global search ability and the convergence speed are both improved compared to the basic optimizers.
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Chen, Q., Sun, J., Palade, V., Li, C., Mao, Z., Wu, H. (2019). Hierarchical Competition Framework for Particle Swarm Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_15
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