High Performance Ant Colony Optimizer (HPACO) for Travelling Salesman Problem (TSP)

被引：0
作者：
Sahana, Sudip Kumar [1 ]
Jain, Aruna [1 ]
机构：
[1] Birla Inst Technol, Dept CSE, Ranchi, Jharkhand, India
来源：
ADVANCES IN SWARM INTELLIGENCE, PT1 | 2014年 / 8794卷
关键词：
Travelling Salesman Problem (TSP); Ant Colony Optimization (ACO); Combinatorial Optimization (CO); Pheromone; Meta-heuristics; ALGORITHM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Travelling Salesman Problem (TSP) is a classical combinatorial optimization problem. This problem is NP-hard in nature and is well suited for evaluation of unconventional algorithmic approaches based on natural computation. Ant Colony Optimization (ACO) technique is one of the popular unconventional optimization technique to solve this problem. In this paper, we propose High Performance Ant Colony Optimizer (HPACO) which modifies conventional ACO. The result of implementation shows that our proposed technique has a better performance than the conventional ACO.
引用
收藏
页码：165 / 172
页数：8
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