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.

粒子群优化（PSO）是一种基于群体的随机优化技术，通过鸟群和鱼类群落的社会行为。PSO 具有较高的收敛速度。它容易在迭代优化过程中失去多样性，并可能陷入糟糕的局部最优。克服这些缺陷仍然是 PSO 应用中的一个重要问题。相比之下，回溯搜索优化算法（BSA）具有强大的全局探索能力，而局部开发能力低且收敛缓慢。针对原有PSO算法通过邻域修改BSA的变异和交叉算子以提高收敛速度的问题，提出了一种改进的带有BSA的PSO算法，称为PSOBSA。在此之上，引入了新的变异算子以提高收敛精度并避免局部最优。几个基准问题用于测试所提出的 PSOBSA 的性能和效率。实验结果表明，PSOBSA 在全局探索能力和准确性以及几乎所有基准问题的收敛速度方面都优于其他著名的元启发式算法和几种最先进的 PSO 变体。