Particle swarm optimizer with self-adjusting neighborhoods

Authors:
Ziyu Chen;Zhongshi He;Cheng Zhang
Affiliations:
Chongqing University, Chongqing, China;Chongqing University, Chongqing, China;Chongqing University, Chongqing, China
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 8
Cited 1

The particle swarm optimization algorithm: convergence analysis and parameter selection

Information Processing Letters
Population structure and particle swarm performance

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Social interaction in particle swarm optimization, the ranked FIPS, and adaptive multi-swarms

Proceedings of the 10th annual conference on Genetic and evolutionary computation
Particle swarm optimization with adaptive population size and its application

Applied Soft Computing
The non-clique particle swarm optimizer

Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Particle swarm optimisation with gradually increasing directed neighbourhoods

Proceedings of the 13th annual conference on Genetic and evolutionary computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Aiming to keep a balance between exploration and exploitation capability, the paper presents a particle swarm optimizer with self-adjusting neighborhoods (PSOSN). In the new algorithm, the particles are initially arranged in ring topology and then automatically adjust their own neighborhood structure based on novel neighborhood extension and restriction strategies. For efficiently controlling the process of information diffusion, neighborhood extension factor (NEF) and local impact factor (LIF) are introduced to depict particle's extension state and neighborhood relation, respectively. The experiment results demonstrate good performance of PSOSN on five benchmark functions compared with the PSO algorithms using different neighborhood schemes.