基于粒子群算法智能算法的TSP问题优化  

Optimizing Traveling Salesman Problem(TSP)Based on Particle Swarm Algorithm Intelligent Algorithm

作　　者：陈劲松[1] Chen Jinsong(School of Management Science and Engineering,Anhui University of Finance and Economics,Bengbu 233030,China)

机构地区：[1]安徽财经大学管理科学与工程学院电子信息系,安徽蚌埠233030

出　　处：《黑河学院学报》2024年第10期177-181,共5页Journal of Heihe University

基　　金：安徽省教育厅自然科学研究项目“异构通用元素型高性能SRAM存内计算芯片研究”(2022AH050595)。

摘　　要：旅行商问题(TSP)是组合优化领域最著名的问题之一,具有深远的理论意义,并在现实应用中产生广泛影响。本研究采用基于粒子群优化(PSO)的智能算法来解决TSP问题。PSO算法利用集体智慧寻找最优解,相比传统方法,能更快速地接近最优解,特别是在处理大规模问题时表现突出。此外,PSO算法不需要问题的具体细节知识,如梯度信息,因此非常适合解决复杂优化任务,这些任务难以通过精确的数学模型定义。将PSO算法应用于TSP问题不仅展示了其在具体问题上的有效性,还证明了该算法在处理更广泛复杂优化问题上的潜力和适应性。这突显了群体智能算法解决实际问题的能力,为解决其他复杂问题提供了有价值的启示。The Traveling Salesman Problem(TSP)is one of the most famous problems in combinatorial optimization,with profound theoretical signifi cance and wide-ranging practical implications.This study employs an intelligent algorithm based on Particle Swarm Optimization(PSO)to tackle the TSP.The PSO algorithm utilizes collective intelligence to search for the optimal solution and,compared to traditional methods,can more rapidly approach the optimal solution,particularly when dealing with large-scale problems.Furthermore,the PSO algorithm does not require detailed knowledge of the problem,such as gradient information,making it well-suited for solving complex optimization tasks that are diffi cult to defi ne precisely using mathematical models.Applying the PSO algorithm to the TSP can demonstrate its eff ectiveness in specifi c problems and showcase its potential and adaptability in tackling a broader range of complex optimization problems,which highlights the capability of swarm intelligence algorithms in solving real-world problems and provides valuable insights for addressing other complex problems.

关 键 词：TSP 粒子群算法 智能算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]