将粒子群优化思想融入遗传算法交叉操作，提升搜索效率与稳定性。
Accelerating Evolution: Integrating PSO Principles into Real-Coded Genetic Algorithm Crossover
- 引入基于粒子群的交叉机制，利用当前及历史最优解引导搜索。
- 在15个测试函数上表现更优，收敛更快且结果更稳定。
- 提供参数调优建议，适合复杂优化问题求解者参考。
本研究提出一种名为粒子群优化启发式交叉（PSOX）的新交叉算子，专为实数编码遗传算法设计。不同于传统仅在同代个体间交换信息的方法，PSOX创新性地融合了当前全局最优解及多代历史最优解的指导作用。该机制在保持种群多样性的同时，加速向搜索空间中的有希望区域收敛。通过在15个具有不同特性的基准测试函数（包括单峰、多峰及高度复杂地形）上的全面实验验证，与五种前沿交叉算子对比，PSOX在解精度、算法稳定性和收敛速度方面均表现更优，尤其在搭配合适变异策略时优势显著。研究还深入分析了不同变异率对PSOX性能的影响，为应对不同景观特性的优化问题提供了实用的参数调优指南。
原文摘要 · Abstract (English)
This study introduces an innovative crossover operator named Particle Swarm Optimization-inspired Crossover (PSOX), which is specifically developed for real-coded genetic algorithms. Departing from conventional crossover approaches that only exchange information between individuals within the same generation, PSOX uniquely incorporates guidance from both the current global best solution and historical optimal solutions across multiple generations. This novel mechanism enables the algorithm to maintain population diversity while simultaneously accelerating convergence toward promising regions of the search space. The effectiveness of PSOX is rigorously evaluated through comprehensive experiments on 15 benchmark test functions with diverse characteristics, including unimodal, multimodal, and highly complex landscapes. Comparative analysis against five state-of-the-art crossover operators reveals that PSOX consistently delivers superior performance in terms of solution accuracy, algorithmic stability, and convergence speed, especially when combined with an appropriate mutation strategy. Furthermore, the study provides an in-depth investigation of how different mutation rates influence PSOX's performance, yielding practical guidelines for parameter tuning when addressing optimization problems with varying landscape properties.
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