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- W3122970619 abstract "Numerous particle swarm optimization (PSO) algorithms have been developed for solving numerical optimization problems in recent years. However, most of existing PSO algorithms have only one search phase. There is no strengthened search phase for the well-performed particles, and also no re-initialization phase for the exhausted particles. These issues may still restrict the performance of PSO for complex optimization problems. In this paper, inspired by the bee-foraging search mechanism of artificial bee colony algorithm, a novel bee-foraging learning PSO (BFL-PSO) algorithm is proposed. Different from existing PSO algorithms, the proposed BFL-PSO has three different search phases, namely employed learning, onlooker learning and scout learning. The employed learning phase works like traditional one-phase-based PSO, while the onlooker learning phase performs strengthened search around those well-performed particles to exploit promising solutions, and the scout learning phase re-initializes those exhausted particles to introduce new diversity. The proposed BFL-PSO is comprehensively evaluated on CEC2014 benchmark functions, and compared with state-of-the-art PSO algorithms as well as artificial bee colony algorithms. The experimental results show that BFL-PSO achieves very competitive performance in terms of solution accuracy. In addition, the effectiveness of the newly introduced onlooker learning and scout learning phases in BFL-PSO is verified. • Bee-foraging search mechanism is integrated into particle swarm optimization. • Bee-foraging learning particle swarm optimization (BFL-PSO) algorithm is developed. • Three different learning phases (employed, onlooker and scout) are adopted in BFL-PSO. • BFL-PSO exhibits competitive performance for complex optimization problems." @default.
- W3122970619 created "2021-02-01" @default.
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- W3122970619 creator A5063436972 @default.
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- W3122970619 date "2021-04-01" @default.
- W3122970619 modified "2023-09-29" @default.
- W3122970619 title "Bee-foraging learning particle swarm optimization" @default.
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- W3122970619 doi "https://doi.org/10.1016/j.asoc.2021.107134" @default.
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