Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

中文翻译：

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工程结构设计的混合动力概率突变粒子群优化算法

粒子群优化（PSO）是一种常见的元启发式算法。但是，当处理实际的工程结构优化问题时，它在搜索过程中易于过早收敛，并陷入局部最优状态。为了增强其性能，结合差分进化算法（DE）的几种思想，提出了一种具有混沌惯性权重（CWDEPSO）的动态概率突变粒子群算法。通过改进本文的参数和算法机制可以实现主要的改进。前者提出了一种新颖的反正切混沌惯性权重和正弦学习因子。此外，比例因子和交叉概率分别通过随机分布得到改善。后者引入了一种监视机制。通过监视PSO的收敛，采用了具有更可靠的局部搜索功能的发达的变异算子，并增加了种群多样性，以帮助PSO有效地摆脱局部最优。为了评估CWDEPSO算法的有效性，分别使用了24个基准函数和两组工程优化实验进行了数值和工程优化。结果表明，与某些著名的元启发式算法相比，CWDEPSO具有更好的收敛精度和速度。分别。结果表明，与某些著名的元启发式算法相比，CWDEPSO具有更好的收敛精度和速度。分别。结果表明，与某些著名的元启发式算法相比，CWDEPSO具有更好的收敛精度和速度。