Novel Ant Colony Optimization Algorithm Based on Order Optimization

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Novel Ant Colony Optimization Algorithm Based on Order Optimization

更新时间：2025-07-09

- Novel Ant Colony Optimization Algorithm Based on Order Optimization
- Vol. 44, Issue 2, Pages: 15-19+30(2010)
- 作者机构：
  
  1. 西安交通大学系统工程研究所,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Online First：10 February 2010，
  
  Published：2010
- 稿件说明：
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Novel Ant Colony Optimization Algorithm Based on Order Optimization[J]. 2010, 44(2): 15-19+30.
DOI：

Novel Ant Colony Optimization Algorithm Based on Order Optimization[J]. 2010, 44(2): 15-19+30. DOI：

摘要

为了评价蚁群算法在有限时间内所得优解的质量

基于序优化方法提出了一种改进的蚁群算法:使用盲目挑选规则选择初始解

并对信息素进行相应的初始化; 确定得到满足要求的优解所需要的迭代次数

将其作为算法的终止条件; 为了更好地利用每次迭代中的优解

在算法开始阶段使用前l个迭代优解更新信息素

以增强探索能力; 在算法结束阶段采用当前迭代最优解更新信息素

以加快收敛速度. 改进算法在保证收敛的前提下

并没有增加算法的时间复杂度. 对旅行商问题进行的仿真实验表明

改进算法在解的质量和收敛速度方面优于最大-最小蚂蚁系统.

Abstract

To evaluate the quality of optimal solutions obtained by the ant colony optimization(ACO)algorithm in limited time

an improved ACO algorithm is presented on the basis of the ordinal optimization. An initial solution is selected using the blind picking rule

and the pheromone is initialized correspondingly. The number of iterations to achieve the optimal solution meeting the demand is then determined and is used as the termination condition of the algorithm. To make better use of the solutions obtained at each iteration

the first l solutions are employed to enhance search capability at the beginning phase of the algorithm. While the current optimal solution is used at the end phase of the algorithm to accelerate the convergence. The time complexity of the novel algorithm is not increased under the condition that ensures the convergence. Simulation results on the traveling salesman problem show that the proposed algorithm is superior to the max-min ant system in both the quality of solutions and the speed of convergence.

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references

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Related Institution

西安交通大学系统工程研究所

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