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An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems

by Ashish Mani, C. Patvardhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 22
Year of Publication: 2010
Authors: Ashish Mani, C. Patvardhan
10.5120/444-677

Ashish Mani, C. Patvardhan . An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems. International Journal of Computer Applications. 1, 22 ( February 2010), 43-48. DOI=10.5120/444-677

@article{ 10.5120/444-677,
author = { Ashish Mani, C. Patvardhan },
title = { An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 22 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 43-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number22/444-677/ },
doi = { 10.5120/444-677 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:47:59.291376+05:30
%A Ashish Mani
%A C. Patvardhan
%T An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 22
%P 43-48
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Real world problems in engineering domain are typically constraint optimization problems. An Adaptive Quantum Evolutionary Algorithm for solving such problems is proposed in this paper. The proposed technique uses a novel qubits representation for search and optimization and uses feasibility rules for handling constraints. Moreover, it does not need stochastic ranking or niching or other methods for maintaining diversity. It does not even require mutation and local heuristics. The algorithm is tested on a standard set of four widely studied benchmark engineering design optimization problems. The results obtained are better than the existing state of the art approaches. The proposed algorithm is simple in concept and implementation, while being robust.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Quantum Evolutionary Algorithm Engineering Optimization Constraint Handling

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