An Improved Quantum-behaved Particle Swarm Optimization Algorithm for the Knapsack Problem

被引：0
|
作者：
Li Xinran [1 ]
机构：
[1] North Univ China, Sch Elect & Comp Sci & Technol, Taiyuan 030051, Shanxi, Peoples R China
来源：
MECHATRONICS, ROBOTICS AND AUTOMATION, PTS 1-3 | 2013年 / 373-375卷
关键词：
Quantum-behaved Particle Swarm Optimization; Self-adaptive; Inertia weight; Slowly varying function; knapsack problem;
D O I：
10.4028/www.scientific.net/AMM.373-375.1178
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An improved Quantum-behaved particle swarm optimization algorithm is proposed for 0-1 knapsack problem.In the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. The simulation experiments show that improved Quantum-behaved particle swarm optimization algorithm has better convergence and stability in solving knapsack problem.
引用
收藏
页码：1178 / 1181
页数：4
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