Bee-foraging learning particle swarm optimization

Xu Chen*, Hugo Tianfield, Wenli Du

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
66 Downloads (Pure)


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.
Original languageEnglish
Article number107134
Number of pages18
JournalApplied Soft Computing
Early online date23 Jan 2021
Publication statusPublished - Apr 2021


  • Artificial bee colony
  • Bee-foraging learning mechanism
  • Numerical optimization
  • Particle swarm optimization

ASJC Scopus subject areas

  • Software


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