2010 2nd International Conference on Information Technology Convergence and Services 2010
DOI: 10.1109/itcs.2010.5581278
View full text |Buy / Rent full text
|
Sign up to set email alerts
|

Abstract: In this paper, we propose a novel evolutionary computing method which is called quantum-inspired electromagnetism-like mechanism (QEM) to solve 0/1 knapsack problem. QEM is based on the electromagnetism theory and using the characteristic of quantum computing. It can rapidly and efficiently find out the optimal solution of combination optimization problem. We compare the conventional genetic algorithm (CGA), quantum-inspired genetic algorithm (QGA), quantum-inspired electromagnetism-like mechanism algorithm (Q… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2017
2017
2017
2017

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 17 publications
(1 reference statement)
0
1
0
Order By: Relevance
“…The attraction and repulsion forces between electrons accumulate into total force in order to make the path result to better direction. Because EM algorithm has good computing ability, there are many kinds of applications, such as optimization question for communication system [6] [7], optimization the neural networks [8], optimization the learning rule for fuzzy neural networks [9], path-tracking problem [10], optimized tool path planning [11], parameter optimization [12], the routing problem [13], optimization the knapsack problem [14] and other applications [15] [16].…”
Section: Electromagnetism-like Mechanism Algorithmmentioning
confidence: 99%
“…The attraction and repulsion forces between electrons accumulate into total force in order to make the path result to better direction. Because EM algorithm has good computing ability, there are many kinds of applications, such as optimization question for communication system [6] [7], optimization the neural networks [8], optimization the learning rule for fuzzy neural networks [9], path-tracking problem [10], optimized tool path planning [11], parameter optimization [12], the routing problem [13], optimization the knapsack problem [14] and other applications [15] [16].…”
Section: Electromagnetism-like Mechanism Algorithmmentioning
confidence: 99%