Cooperative Compounded Particle Swarm Optimization and application

Hongbo Wang, Kezheng Wang, Y. Xue, Xuyan Tu
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Abstract

In real-time high dimensions optimization problem, how to quickly find the optimal solution and give timely response or decisive adjustment is very important. Inspired by the mutual parasitic behaviors, this paper suggests a new PSO variant, Cooperative Compounded Particle Swarm Optimization (COMPSO) that improves the convergence speed and reduces the possibility of particles into the local optimum. By using of real encoding mechanism, COMPSO is applied to the vehicle routing problem. Compared with other PSO algorithms, experimental results show the superiority of COMPSO algorithm in terms of the solution quality and computational efficiency. It proves a helpful guiding significance.
协同复合粒子群优化及其应用
在实时高维优化问题中,如何快速找到最优解并给予及时响应或果断调整是非常重要的。受相互寄生行为的启发,本文提出了一种新的粒子群优化算法——协同复合粒子群优化算法(Cooperative composite Particle Swarm Optimization, COMPSO),该算法提高了粒子群的收敛速度,减少了粒子陷入局部最优的可能性。利用实数编码机制,将COMPSO应用于车辆路径问题。实验结果表明,与其他粒子群算法相比,COMPSO算法在解质量和计算效率方面具有优势。具有一定的指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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