Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

被引：1
|
作者：
Liu Lan Juan [1 ]
Li Bao Lei [1 ]
Zhang Qin Hu [1 ]
Lv Dan Jv [1 ]
Shi Xin Lin [1 ]
Li Jing Jing [1 ]
机构：
[1] Yunnan Univ, Dept Elect Engn, Informat Sch, Kunming 650091, Yunnan, Peoples R China
来源：
MECHATRONICS ENGINEERING, COMPUTING AND INFORMATION TECHNOLOGY | 2014年 / 556-562卷
关键词：
Knapsack problem; Multivariant optimization algorithm; Multivariant search groups; Global exploration; Local exploration;
D O I：
10.4028/www.scientific.net/AMM.556-562.3514
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.
引用
收藏
页码：3514 / 3518
页数：5
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