Hybrid particle swarm optimization and pattern search algorithm

Particle swarm optimization (PSO) is one of the most commonly used stochastic optimization algorithms for many researchers and scientists of the last two decades, and the pattern search (PS) method is one of the most important local optimization algorithms. In this paper, we test three methods of hy...

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Bibliographic Details
Published in:Optimization and engineering Vol. 22; no. 3; pp. 1539 - 1555
Main Authors: Koessler, Eric, Almomani, Ahmad
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2021
Springer Nature B.V
Subjects:
Control
Engineering
Environmental Management
Financial Engineering
Genetic algorithms
Local optimization
Mathematics
Mathematics and Statistics
Minima
Operations Research/Decision Theory
Optimization
Optimization algorithms
Particle swarm optimization
Pattern search
Research Article
Robustness (mathematics)
Search algorithms
Systems Theory
Pattern search
Test problem benchmarking
Particle swarm optimization
Hybrid algorithm
Derivative-free optimization
ISSN:1389-4420, 1573-2924
Online Access:Get full text
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Summary:Particle swarm optimization (PSO) is one of the most commonly used stochastic optimization algorithms for many researchers and scientists of the last two decades, and the pattern search (PS) method is one of the most important local optimization algorithms. In this paper, we test three methods of hybridizing PSO and PS to improve the global minima and robustness. All methods let PSO run first followed by PS. The first method lets PSO use a large number of particles for a limited number of iterations. The second method lets PSO run normally until tolerance is reached. The third method lets PSO run normally until the average particle distance from the global best location is within a threshold. Numerical results using non-differentiable test functions reveal that all three methods improve the global minima and robustness versus PSO. The third hybrid method was also applied to a basin network optimization problem and outperformed PSO with filter method and genetic algorithm with implicit filtering.
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ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-020-09534-7