Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm

Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm

This paper studies and evaluates a fitness-based crossover operator in an evolutionary multi-objective optimization algorithm, which heuristically optimizes the sensing coverage area and the installation cost in wireless sensor networks. The proposed evolutionary algorithm uses a population of indiv...

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Main Authors: Paskorn Champrasert, Teerawat Kumrai
Format: Conference Proceeding
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885973382&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/47560
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Institution: Chiang Mai University
id th-cmuir.6653943832-47560
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spelling th-cmuir.6653943832-475602018-04-25T08:41:23Z Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm Paskorn Champrasert Teerawat Kumrai This paper studies and evaluates a fitness-based crossover operator in an evolutionary multi-objective optimization algorithm, which heuristically optimizes the sensing coverage area and the installation cost in wireless sensor networks. The proposed evolutionary algorithm uses a population of individuals (or chromosomes), each of which represents a set of wireless sensor nodes' types and positions, and evolves them via the proposed fitness-based crossover operator (FBX) for seeking optimal sensing coverage and installation cost. Simulation results show that the fitness-based crossover evolutionary algorithm outperforms a well-known existing evolutionary algorithm for multi-objective optimization. © 2013 IEEE. 2018-04-25T08:41:23Z 2018-04-25T08:41:23Z 2013-10-28 Conference Proceeding 2-s2.0-84885973382 10.1109/TIME-E.2013.6611969 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885973382&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/47560
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
description This paper studies and evaluates a fitness-based crossover operator in an evolutionary multi-objective optimization algorithm, which heuristically optimizes the sensing coverage area and the installation cost in wireless sensor networks. The proposed evolutionary algorithm uses a population of individuals (or chromosomes), each of which represents a set of wireless sensor nodes' types and positions, and evolves them via the proposed fitness-based crossover operator (FBX) for seeking optimal sensing coverage and installation cost. Simulation results show that the fitness-based crossover evolutionary algorithm outperforms a well-known existing evolutionary algorithm for multi-objective optimization. © 2013 IEEE.
format Conference Proceeding
author Paskorn Champrasert
Teerawat Kumrai
spellingShingle Paskorn Champrasert
Teerawat Kumrai
Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
author_facet Paskorn Champrasert
Teerawat Kumrai
author_sort Paskorn Champrasert
title Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
title_short Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
title_full Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
title_fullStr Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
title_full_unstemmed Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
title_sort coverage and installation cost optimization in wsns using a fitness-based crossover evolutionary algorithm
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885973382&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/47560
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