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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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
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Abstract	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.
AbstractList	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.
Author	Koessler, Eric Almomani, Ahmad
Author_xml	– sequence: 1 givenname: Eric surname: Koessler fullname: Koessler, Eric organization: Mathematics Department, State University of New York at Geneseo – sequence: 2 givenname: Ahmad surname: Almomani fullname: Almomani, Ahmad email: almomani@geneseo.edu organization: Mathematics Department, State University of New York at Geneseo
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Cites_doi	10.1109/AISP.2012.6313781 10.1007/s11771-014-2291-y 10.1145/321062.321069 10.1504/IJMMNO.2013.055204 10.1007/s10898-007-9133-5 10.1007/978-3-319-72550-5_2 10.1007/978-0-387-30164-8_630 10.1061/(ASCE)IR.1943-4774.0001310 10.1016/j.apenergy.2009.10.007
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References_xml	– reference: Meftahi M, Jazi SH (2012) A new hybrid algorithm of pattern search and abc for optimization. In: The 16th CSI international symposium on artificial intelligence and signal processing (AISP 2012), pp 403–406 – reference: Kennedy J (2010) Particle swarm optimization. Encyclopedia of machine learning, pp 760–766 – reference: MirjaliliSAMohd HashimSZTraining feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithmAppl Math Comput201221822111251113729423951282.90248 – reference: Wahid F, Ghazali R (2018) Hybrid of firefly algorithm and pattern search for solving optimization problems. Evolut Intell – reference: Ansari Ardeh M. Benchmarkfcns toolbox – reference: Ritz B (2017) A hybrid genetic algorithm with implicit filtering for mixed integer optimization problem. PhD thesis, Clarkson University, Potsdam, NY – reference: BeauregardJRitzBW JenkinsER KavanaghKW FarthingMOptimization of a basin network using hybridized global search algorithmsJ Irrig Drain Eng201814480401801710.1061/(ASCE)IR.1943-4774.0001310 – reference: Jamil M, Yang X-S (2013). A literature survey of benchmark functions for global optimization problems. arXiv preprint arXiv:1308.4008 – reference: Weihang Z, Curry J (2009) Particle swarm with graphics hardware acceleration and local pattern search on bound constrained problems. In: 2009 IEEE swarm intelligence symposium, SIS 2009–proceedings, pp 1 – 8 – reference: Ismael F. VazAVicenteLNA particle swarm pattern search method for bound constrained global optimizationJ Global Optim2007392197219233637110.1007/s10898-007-9133-5 – reference: AlsumaitJSSykulskiJKAl-OthmanAKA hybrid ga-ps-sqp method to solve power system valve-point economic dispatch problemsAppl Energy20108751773178110.1016/j.apenergy.2009.10.007 – reference: HookeRA JeevesT”direct search” solution of numerical and statistical problemsJ ACM (JACM)19618221222910.1145/321062.321069 – reference: McCartJAlmomaniANew criteria for comparing global stochastic derivative-free optimization algorithms(IJACSA) Int J Adv Comput Sci Appl2019107614625 – reference: Almomani A (2012) Constraint handling for derivative-free optimization. PhD thesis, Clarkson University, Potsdam, NY – reference: Clerc M et al. (2007) Standard pso 2007. Particle Swarm Central Website – reference: KhadangaRKSatapathyJKA hybrid gravitational search and pattern search algorithm for tuning damping controller parameters for a unified power flow controller-a comparative approachInt J Numer Modell Electron Netw Dev Fields2018313e231 – reference: LongWZhangWHuangYChenYA hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimizationJ Central South Univ20142183197320410.1007/s11771-014-2291-y – reference: Matlab, MATLAB2017b, The MathWorks, Natick, MA, USA – ident: 9534_CR13 doi: 10.1109/AISP.2012.6313781 – ident: 9534_CR3 – volume: 21 start-page: 3197 issue: 8 year: 2014 ident: 9534_CR10 publication-title: J Central South Univ doi: 10.1007/s11771-014-2291-y – ident: 9534_CR18 – ident: 9534_CR5 – volume: 218 start-page: 11125 issue: 22 year: 2012 ident: 9534_CR14 publication-title: Appl Math Comput – ident: 9534_CR1 – volume: 8 start-page: 212 issue: 2 year: 1961 ident: 9534_CR6 publication-title: J ACM (JACM) doi: 10.1145/321062.321069 – ident: 9534_CR7 doi: 10.1504/IJMMNO.2013.055204 – volume: 31 start-page: e231 issue: 3 year: 2018 ident: 9534_CR9 publication-title: Int J Numer Modell Electron Netw Dev Fields – volume: 39 start-page: 197 issue: 2 year: 2007 ident: 9534_CR16 publication-title: J Global Optim doi: 10.1007/s10898-007-9133-5 – ident: 9534_CR17 doi: 10.1007/978-3-319-72550-5_2 – ident: 9534_CR8 doi: 10.1007/978-0-387-30164-8_630 – volume: 144 start-page: 04018017 issue: 8 year: 2018 ident: 9534_CR4 publication-title: J Irrig Drain Eng doi: 10.1061/(ASCE)IR.1943-4774.0001310 – volume: 10 start-page: 614 issue: 7 year: 2019 ident: 9534_CR12 publication-title: (IJACSA) Int J Adv Comput Sci Appl – volume: 87 start-page: 1773 issue: 5 year: 2010 ident: 9534_CR2 publication-title: Appl Energy doi: 10.1016/j.apenergy.2009.10.007 – ident: 9534_CR15 – ident: 9534_CR11
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SubjectTerms	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
Title	Hybrid particle swarm optimization and pattern search algorithm
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