Adaptive Ant Colony Optimization Algorithm Based on Maturity

WANG Hai-ning; SUN Shou-qian

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Adaptive Ant Colony Optimization Algorithm Based on Maturity

更新时间：2026-09-04

- Adaptive Ant Colony Optimization Algorithm Based on Maturity
- Vol. 27, Issue 8, Pages: 140-144(2010)
- 作者机构：
  
  浙江大学计算机学院,浙江,杭州,310027
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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王海宁, 孙守迁. 基于优化成熟度的自适应蚁群优化算法[J]. 微电子学与计算机, 2010, 27(8): 140-144.

WANG Hai-ning, SUN Shou-qian. Adaptive Ant Colony Optimization Algorithm Based on Maturity[J]. 2010, 27(8): 140-144.
王海宁, 孙守迁. 基于优化成熟度的自适应蚁群优化算法[J]. 微电子学与计算机, 2010, 27(8): 140-144. DOI：

WANG Hai-ning, SUN Shou-qian. Adaptive Ant Colony Optimization Algorithm Based on Maturity[J]. 2010, 27(8): 140-144. DOI：

摘要

分析了蚁群算法局部信息素更新系数与全局信息素更新系数对算法寻优能力与收敛速度的关系

定义平均路径相似度(ATS)来表征寻优过程的成熟程度

并据此自适应调整信息素更新系数

提高算法收敛速度并避免陷入局部最优.经过与典型蚁群算法在多个旅行商问题测试用例上进行收敛速度与全局寻优能力的全面比较

证明了新的算法具有较好的效果.

Abstract

By observing the effect of parameters on the performance of the ACS algorithm in different optimization state

this paper presents a novel version of ACS based on the optimization maturity for obtaining self-adaptive parameters control. The adaptive ACS has been applied to optimize several benchmark TSP instances. The solution quality

convergence rate and global searching ability are favorably compared with the ACS. Experimental results confirm that our proposed method is effective and outperforms the conventional ACS.

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Related Author

LU Xiu-ying

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Related Institution

Dean’s Office, Agricultural University of Hebei

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