An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems

The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor loca...

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Published in:	Engineering with computers Vol. 38; no. Suppl 4; pp. 2797 - 2831
Main Authors:	Zaman, Hamid Reza Rafat, Gharehchopogh, Farhad Soleimanian
Format:	Journal Article
Language:	English
Published:	London Springer London 01.10.2022 Springer Nature B.V
Subjects:	Algorithms Benchmarks CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Convergence Heuristic methods Math. Applications in Chemistry Mathematical and Computational Engineering Mutation Optimization algorithms Optimization techniques Original Article Particle swarm optimization Systems Theory Hybridization Solving continuous optimization problems Particle swarm optimization Backtracking search optimization algorithm
ISSN:	0177-0667, 1435-5663
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Abstract	The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor local optimum. Overcoming these defects is still a significant problem in PSO applications. In contrast, the backtracking search optimization algorithm (BSA) has a robust global exploration ability, whereas, it has a low local exploitation ability and converges slowly. This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm’s problems that BSA’s mutation and crossover operators were modified through the neighborhood to increase the convergence rate. In addition to that, a new mutation operator was introduced to improve the convergence accuracy and evade the local optimum. Several benchmark problems are used to test the performance and efficiency of the proposed PSOBSA. The experimental results show that PSOBSA outperforms other well-known metaheuristic algorithms and several state-of-the-art PSO variants in terms of global exploration ability and accuracy, and rate of convergence on almost all of the benchmark problems.
AbstractList	The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor local optimum. Overcoming these defects is still a significant problem in PSO applications. In contrast, the backtracking search optimization algorithm (BSA) has a robust global exploration ability, whereas, it has a low local exploitation ability and converges slowly. This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm’s problems that BSA’s mutation and crossover operators were modified through the neighborhood to increase the convergence rate. In addition to that, a new mutation operator was introduced to improve the convergence accuracy and evade the local optimum. Several benchmark problems are used to test the performance and efficiency of the proposed PSOBSA. The experimental results show that PSOBSA outperforms other well-known metaheuristic algorithms and several state-of-the-art PSO variants in terms of global exploration ability and accuracy, and rate of convergence on almost all of the benchmark problems.
Author	Gharehchopogh, Farhad Soleimanian Zaman, Hamid Reza Rafat
Author_xml	– sequence: 1 givenname: Hamid Reza Rafat surname: Zaman fullname: Zaman, Hamid Reza Rafat organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University – sequence: 2 givenname: Farhad Soleimanian orcidid: 0000-0003-1588-1659 surname: Gharehchopogh fullname: Gharehchopogh, Farhad Soleimanian email: bonab.farhad@gmail.com organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University
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Keywords	Hybridization Solving continuous optimization problems Particle swarm optimization Backtracking search optimization algorithm
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Snippet	The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The...
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SubjectTerms	Algorithms Benchmarks CAE) and Design Calculus of Variations and Optimal Control; Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Convergence Heuristic methods Math. Applications in Chemistry Mathematical and Computational Engineering Mutation Optimization algorithms Optimization techniques Original Article Particle swarm optimization Systems Theory
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Title	An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems
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