Journal
ENGINEERING WITH COMPUTERS
Volume 38, Issue SUPPL 4, Pages 2797-2831Publisher
SPRINGER
DOI: 10.1007/s00366-021-01431-6
Keywords
Particle swarm optimization; Backtracking search optimization algorithm; Solving continuous optimization problems; Hybridization
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This paper introduces the advantages and disadvantages of particle swarm optimization (PSO) and backtracking search optimization algorithm (BSA), proposes an improved algorithm PSOBSA to address the issues of PSO algorithm, and validates its superior performance through experiments.
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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