2022
DOI: 10.1299/jamdsm.2022jamdsm0035
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Adaptive heterogeneous particle swarm optimization with comprehensive learning strategy

Abstract: This paper proposes an adaptive heterogeneous particle swarm optimization with a comprehensive learning strategy for solving single-objective constrained optimization problems. In this algorithm, particles can use an exploration strategy and an exploitation strategy to update their positions. The historical success rates of the two strategies are used to adaptively control the adoption rates of strategies in the next iteration. The search strategy in the canonical particle swarm optimization algorithm is based… Show more

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Cited by 3 publications
(2 citation statements)
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“…It models the network as a search space and the call admission policies as potential solutions to the problem [82]. The particles in the swarm represent different call admission policies, and their movement is influenced by their own personal best solution, the best solution of the swarm and the current state of the network.…”
Section: Proposed Algorithmmentioning
confidence: 99%
“…It models the network as a search space and the call admission policies as potential solutions to the problem [82]. The particles in the swarm represent different call admission policies, and their movement is influenced by their own personal best solution, the best solution of the swarm and the current state of the network.…”
Section: Proposed Algorithmmentioning
confidence: 99%
“…To solve the problem that the PSO algorithm can become trapped near a local optimum, Liu and Nishi [33] proposed a new update formula for Pbest, which expands the search range of particles by searching adjacent positions of Pbest to avoid premature convergence to some extent. Lee et al [34] added a random noise term to the velocity update formula of PSO and proposed the repulsive method based on the repulsion theory applied to the PSO algorithm.…”
Section: ) Topology Improvementmentioning
confidence: 99%