A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization

In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization phase, an opposition learning strategy is performed to improve the search efficiency....

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Vydané v:	Journal of global optimization Ročník 55; číslo 1; s. 165 - 188
Hlavný autor:	Kaucic, Massimiliano
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.01.2013 Springer Springer Nature B.V
Predmet:	Adaptive algorithms Algorithms Analysis Computer Science Convergence Genetic algorithms Mathematical optimization Mathematics Mathematics and Statistics Mutation Neighborhoods Operations Research/Decision Theory Optimization Optimization algorithms Real Functions Robustness Stagnation Strategy Swarm intelligence Velocity Hybrid methods Restart techniques Opposition-based computing Bound constrained optimization Particle swarm optimization
ISSN:	0925-5001, 1573-2916
On-line prístup:	Získať plný text
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