基于偏序对改进蝙蝠算法的旅行商问题研究

作为一种新兴的群智能启发式算法,蝙蝠算法近年来被广泛用于求解离散、连续、及组合优化问题。针对典型组合优化问题中的旅行商问题,本文提出一种基于偏序对改进的蝙蝠算法用于求解离散型旅行商问题。通过对蝙蝠速度、位置的更新,使算法具有更强的适用性。本文对16个标准TSP问题进行测试与对比分析以验证算法有效性。实验结果表明,本文提出的偏序对蝙蝠算法在大多数实例中均优于其他算法。

An Improved Unordered Pair Bat Algorithm for Traveling Salesman Problem

As a novel heuristic algorithm for swarm intelligence, the bat algorithm has been widely used in recent years to solve discrete, continuous, and combinatorial optimization problems. Aiming at the traveling salesman problem which is one of typical combinatorial optimization problems, this paper proposes an improved bat algorithm based on partial order pair for solving discrete traveling salesman problems. This paper proposes an improved bat algorithm based on partial order pairs for solving discrete traveling salesman problems. By updating the bat speed and position, the algorithm has stronger applicability. This paper tests and compares 16 standard TSP problems to verify the effectiveness of the proposed algorithm. The experimental results verify that the partial order improved bat algorithm in this paper is superior to other algorithms in most examples.