一种改进的基于教与学的优化算法求解旅行商问题  

Teaching-Learning Based Optimization Algorithm for Traveling Salesman Problem

作　　者：何湘竹[1] 

机构地区：[1]中南民族大学电子信息工程学院,武汉430074

出　　处：《中南民族大学学报（自然科学版）》2015年第4期89-93,共5页Journal of South-Central University for Nationalities：Natural Science Edition

基　　金：国家重点基础研究发展计划项目(973计划项目)(2011CB706804);教育部中央高校基本科研业务费专项(CZQ12002)

摘　　要：提出了一种改进的基于教与学的优化算法(TLBO)求解旅行商(TSP)问题,阐述了TLBO算法的基本思想和求解步骤,给出了算法流程,针对算法在解决大规模问题时易陷入局部最优的缺陷,引入混沌搜索机制对其进行了改进.着重研究了改进后的TLBO算法求解TSP问题的求解结果和性能分析,通过benchmark实例进行了仿真实验,结果表明:与诸如遗传算法和粒子群优化算法等已有启发式算法相比,改进后的TLBO算法在求解TSP问题时性能更为优越,从而为TSP问题的求解找到了一条新途径.This paper introduced an improved teaching-learning based optimization algorithm into traveling salesman problem,it illustrated the main ideas and the procedure of TLBO,to overcome the shortage of being trapped into local optimum when facing large scale problems,the chaos search mechanism was introduced to improve the performance of TLBO. The paper focused on the result and performance analysis of solving the traveling salesman problem with improved teaching-learning based optimization algorithm, experimental results of some typical benchmarks demonstrated that compared with other heuristic algorithms like GA and PSO,the improved TLBO algorithm achieved a good performance while requiring a much less computation,thus can be served as a new method to solve TSP problem.

关 键 词：旅行商问题 NP完全 传统优化算法 启发式算法 TLBO算法 混沌搜索 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]