An improved ant colony algorithm to solve knapsack problem

被引：0
|
作者：
Li Shuang [1 ,2 ]
Wang Shuliang [3 ]
Zhang Qiuming [1 ]
机构：
[1] China Univ Geosci, Sch Comp, Wuhan 430074, Peoples R China
[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China
[3] Wuhan Univ, Int Sch Software, Wuhan 430072, Peoples R China
来源：
GEOINFORMATICS 2006: REMOTELY SENSED DATA AND INFORMATION | 2006年 / 6419卷
关键词：
ant colony optimization algorithm; Knapsack Problem; evolutionary computing;
D O I：
10.1117/12.713269
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Ant colony optimization algorithm is a novel simulated evolutionary algorithm, which provides a new method for complicated combinatorial optimization problems. In this paper the algorithm is used for solving the knapsack problem. It is improved in selection strategy and information modification, so that it can not easily run into the local optimum and can converge at the global optimum. The experiments show the robustness and the potential power of this kind of meta heuristic algorithm.
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页数：7
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