An expanded particle swarm optimization based on multi-exemplar and forgetting ability

DOI10.1016/J.INS.2019.08.065zbMath1456.90179OpenAlexW2970404044WikidataQ127311682 ScholiaQ127311682MaRDI QIDQ1999088

Yanyan Li

Publication date: 18 March 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2019.08.065

zbMATH Keywords

global optimizationadaptive adjustmentparticle swarm optimizationforgetting abilitymulti-exemplar

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (5)

A novel \(x\)-shaped binary particle swarm optimization ⋮ Multi-sample learning particle swarm optimization with adaptive crossover operation ⋮ Three-learning strategy particle swarm algorithm for global optimization problems ⋮ Lévy flight-based inverse adaptive comprehensive learning particle swarm optimization ⋮ A multi-sample particle swarm optimization algorithm based on electric field force

Cites Work

• Self regulating particle swarm optimization algorithm
• High-order feedback iterative learning control algorithm with forgetting factor
• Particle swarm optimization using dimension selection methods
• The role of forgetting in the evolution and learning of language

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