Combinatorial problem optimization using TLBO

Sahil Saharan, J. Lather, R. Radhakrishnan
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Abstract

Travelling Salesman Problem(TSP) is an NP hard problem, which has been addressed via several heuristic algorithms like genetic algorithms (GA) etc. The paper presents TSP solution using Teaching Learning Based optimization(TLBO) which includes many similarities like the other nature-inspired algorithms. In addition, the strategy adds an auxiliary operator to regulate the sequence of array elements in mutation process with partial-mapped crossover used over the selected candidates of the population. Experimental study shows that introduced strategy can provide better convergence. The prominent feature of the strategy is in its stability and superior ways to handle computational complexity while dealing with TSP optimization as compared to GA.
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基于TLBO的组合问题优化
旅行商问题(TSP)是一个NP困难问题,目前已通过遗传算法(GA)等启发式算法进行求解。本文提出了一种基于教学学习的优化算法(TLBO),它与其他自然启发算法有许多相似之处。此外,该策略还增加了一个辅助算子,通过对选定的候选群体进行部分映射交叉,来调节突变过程中数组元素的序列。实验研究表明,该策略具有较好的收敛性。与遗传算法相比,该策略的突出特点是在处理TSP优化时具有稳定性和处理计算复杂性的优越方式。
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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