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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Vydáno v:Journal of global optimization Ročník 55; číslo 1; s. 165 - 188
Hlavní autor: Kaucic, Massimiliano
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.01.2013
Springer
Springer Nature B.V
Témata:
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
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Shrnutí: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. Then a variant of the adaptive velocity based on the differential operator enhances the optimization ability of the particles. Finally, a re-initialization strategy based on two diversity measures for the swarm is act in order to avoid premature convergence and stagnation. The strategy uses the super-opposition paradigm to re-initialize particles in the swarm. The algorithm has been evaluated on a set of 100 global optimization test problems. Comparisons with other global optimization methods show the robustness and effectiveness of the proposed algorithm.
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-012-9913-4