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蚁群优化算法是一种求解组合优化问题的通用算法框架.取样送检路径规划问题是一种带约束的组合优化问题,本文给出了一种求解该问题的数学模型.为求解该问题提出了一种多启发式信息蚁群优化算法(MACO),在选择下一访问节点的概率计算公式中增加了一项启发式信息——起点到被选择点之间距离的倒数,并从理论上分析了该算法的收敛性.在9个算例上进行了仿真实验和分析,说明了新增启发式信息的有效性和适用性,验证了MACO算法可以有效求解该问题,并能获得质量更好的解.
Ant colony optimization algorithm is a general algorithm framework for solving combinatorial optimization problems.Sample routing problem is a constrained combinatorial optimization problem.This paper presents a mathematical model to solve the problem.To solve this problem A multi-heuristic information-based ant colony optimization algorithm (MACO) adds a heuristic message to the probability calculation formula for selecting the next visited node - the reciprocal of the distance between the starting point and the selected point and theoretically The convergence of the algorithm is analyzed, and the simulation experiments and analysis are carried out on nine examples, which shows the validity and applicability of the new heuristic information. It is verified that the MACO algorithm can effectively solve the problem and obtain better quality Good solution.