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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2018
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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 |
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Chiang Mai University Library |
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Thailand |
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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 |
| _version_ |
1681423083946115072 |