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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Vydáno v:Engineering with computers Ročník 38; číslo Suppl 4; s. 2797 - 2831
Hlavní autoři: Zaman, Hamid Reza Rafat, Gharehchopogh, Farhad Soleimanian
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.10.2022
Springer Nature B.V
Témata:
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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Popis
Shrnutí: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.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-021-01431-6