Enhanced Particle Swarm Optimization With Self-Adaptation Based On Fitness-Weighted Acceleration Coefficients

Wang, Hei-Chia; Yang, Che Tsung

doi:10.1080/10798587.2015.1057956

Cited by 5 publications

(1 citation statement)

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“…Cognition and social velocity models of the swarm indicate the attraction of the particles towards the global best in the feasible neighbourhood and usually converge faster with predominantly exploratory behaviour [25]. Cognitive and social acceleration coefficients are weights that capture how much a particle should weigh moving towards its cognitive attractor or its social attractor [26]. The swarm topologies establish swarm particles connectivity of its members to the others.…”

Section: Preliminary Workmentioning

confidence: 99%

Selection of Building Blocks for Adaptive Grammatical Evolution in PSO

Gaddam

Barca

Dazeley

et al. 2022

Preprint

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In this study, we used grammatical evolution to develop a customised particle swarm optimiser by incorporating adaptive building blocks. This makes the algorithm self-adaptable to the problem instance. Our objective is to provide the means to automatically generate novel population-based meta-heuristics by scoring the building blocks. We propose a new self-adapting algorithm by adaptive selection and scoring of the building blocks to solve multiple problem instances by reducing computation time and iteration count. To achieve our objective, we ranked building blocks that were extracted from a broad set of existing particle swarm optimisers and scored these during the evolutionary process. These scores were provided as an input to the evolutionary process that enabled the replacement of blocks of evolved solutions in cases where they were unable to improve the overall fitness. Our numerical experiments demonstrated that the proposed algorithm with adaptive building blocks reduced the iteration count and computation time with respect to PSO.

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Section: Preliminary Workmentioning

confidence: 99%