Paper Info
Title
Particle swarm optimization with oscillation control
Abstract
Particle Swarm Optimization (PSO) is a metaheuristic that has been successfully applied to linear and non-linear optimization problems in functions with discrete and continuous domains. This paper presents a new variation of this algorithm - called oscPSO - that improves the inherent search capacity of the original (canonical) version of the PSO algorithm. This version uses a deterministic local search method whose use depends on the movement patterns of the particles in each dimension of the problem. The method proposed was assessed by means of a set of complex test functions, and the performance of this version was compared with that of the original version of the PSO algorithm. In all cases, the oscPSO variation equaled or surpassed the performance of the canonical version of the algorithm.
Year
DOI
Venue
2009
10.1145/1569901.1570141
GECCO
Keywords
Field
DocType
particle swarm optimization,deterministic local search method,oscillation control,oscpso variation,pso algorithm,inherent search capacity,canonical version,continuous domain,complex test function,new variation,original version,oscillations,local search,evolutionary computing
Particle swarm optimization,Mathematical optimization,Parallel metaheuristic,Computer science,Meta-optimization,Multi-swarm optimization,Firefly algorithm,Artificial intelligence,Imperialist competitive algorithm,Optimization problem,Machine learning,Metaheuristic
Conference
Citations 
PageRank 
References 
0
0.34
3
Authors
3
Name
Order
Citations
PageRank
Javier H. López120.81
Laura Lanzarini2218.94
Armando Giusti37121.17