Enhanced Ant Colony Optimization Combined with Genetic Algorithm

LIANG Hao-mo; WANG Zhi-xue; LIU Yi

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Enhanced Ant Colony Optimization Combined with Genetic Algorithm

更新时间：2026-09-04

- Enhanced Ant Colony Optimization Combined with Genetic Algorithm
- Vol. 35, Issue 8, Pages: 101-104(2018)
- 作者机构：
  
  解放军理工大学指挥信息系统学院,江苏,南京,210007
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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梁豪默, 王智学, 刘艺. 遗传增强蚁群优化算法[J]. 微电子学与计算机, 2018, 35(8): 101-104, 108.

LIANG Hao-mo, WANG Zhi-xue, LIU Yi. Enhanced Ant Colony Optimization Combined with Genetic Algorithm[J]. 2018, 35(8): 101-104.
梁豪默, 王智学, 刘艺. 遗传增强蚁群优化算法[J]. 微电子学与计算机, 2018, 35(8): 101-104, 108. DOI：

LIANG Hao-mo, WANG Zhi-xue, LIU Yi. Enhanced Ant Colony Optimization Combined with Genetic Algorithm[J]. 2018, 35(8): 101-104. DOI：

摘要

针对蚁群优化算法易陷入局部最优的问题

提出遗传增强蚁群优化算法.在算法迭代过程中

维护历史最优解

将历史最优解与当前部分较好解作为遗传算法的初始化染色体

采用遗传算法在更广阔的空间中搜索较好解.将蚂蚁搜索解与遗传算法搜索解合并选择当前最优解

防止蚁群优化陷入局部最优.以二分类问题中的特征选择为例

与粒子群优化算法与差分进化算法进行对比

在3个标准测试数据上进行试验

结果表明算法的有效性及其优越性.

Abstract

In order to solve the problem that ant colony optimization is easily trapped into local optimum

a new algorithm called enhanced ant colony optimization based on genetic algorithm was proposed. It maintained a history best optimal solution which was combined with part of current best solutions as the initial chromosomes during iterations

and adopted genetic algorithm to search better solutions at a larger space. It selected the current best solution from results of ants and chromosomes to prevent ant colony optimization from being trapped into local optimum. It was tested on feature selection problem of binary classification and compared with particle swarm optimization and differential evolution

the experiments were implemented on three benchmark datasets

and the results show the efficiency and superiority of the proposed algorithm.

关键词

Keywords

references

Liu Y, Diao X, Cao J, et al. Local Enhanced Catfish Bat Algorithm[C]// International Conference on Robots % Intelligent System. 2016: 238-245.

Meng Y S, Enríquez A C, Hsiao T Y, et al. Enhanced Differential Evolution Algorithm for Coordination of Directional Overcurrent Relays[J]. Electric Power Systems Research, 2017, 143:365-375. DOI: 365

Wu M T, Zhan J, Lin C W. Mining of High-Utility Itemsets by ACO Algorithm[C]// The Multidisciplinary International Social Networks Conference. 2016: 44.

Król A. Application of the Genetic Algorithm for Optimization of the Public Transportation Lines[M]// Intelligent Transport Systems and Travel Behavior. Springer International Publishing, 2017.

Shabeera T P, Kumar S D M, Salam S M, et al. Optimizing VM Allocation and Data Placement for Data-intensive Applications in Cloud Using ACO Metaheuristic Algorithm[J]. Engineering Science % Technology An International Journal, 2016.20(2).

Wang B, Zheng X, Zhou S, et al. Constructing DNA Barcode Sets Based on Particle Swarm Optimization.[J]. IEEE/ACM Transactions on Computational Biology % Bioinformatics, 2017, PP(99): 1-1. DOI: 1

Zhang Q, Zhang C. An improved Ant Colony Optimization Algorithm with Strengthened Pheromone Updating Mechanism for Constraint Satisfaction Problem[J]. Neural Computing % Applications, 2017:1-12. DOI: 1

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