Population-Center-Guided Particle Swarm Optimization Algorithm for Global Optimization

传统粒子群优化算法(PSO)在求解复杂优化问题时易陷入局部最优，限制了其全局搜索性能。为提升其全局寻优能力，本文提出面向全局优化的种群中心引导型粒子群优化算法，设计了两种改进机制：一是引入种群中心位置作为额外引导信息，构建粒子群优化–社会影响算法(PSOSI)；二是基于种群聚集程度引入扰动策略，构建粒子群优化–局部扰动算法(PSOLP)。本文在CEC-2022标准测试集及8个实际工程设计问题上对所提算法进行了系统评估。实验结果表明，PSOSI和PSOLP在优化精度和收敛稳定性方面均优于标准PSO及多种主流对比算法，验证了所提方法的有效性与通用性，为解决全局优化与工程优化问题提供了高效可行的工具。Traditional Particle Swarm Optimization (PSO) algorithms tend to fall into local optima when solving complex optimization problems, which hampers their global search capability. To enhance global optimization performance, this paper proposes population-center-guided PSO framework and introduces two improved variants: Particle Swarm Optimization with Social Influence (PSOSI), which incorporates the population center as an additional guiding factor, and Particle Swarm Optimization with Local Perturbation (PSOLP), which introduces a disturbance strategy based on population aggregation. The proposed algorithms are systematically evaluated on the CEC-2022 benchmark suite and eight real-world engineering design problems. Experimental results demonstrate that both PSOSI and PSOLP achieve superior optimization accuracy and convergence stability compared to standard PSO and several state-of-the-art algorithms, validating the effectiveness and generality of the proposed approaches. These methods offer practical tools for addressing global and engineering optimization tasks.