Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/52428
Title: Coverage and installation cost optimization in WSNs using a fitness-based crossover evolutionary algorithm
Authors: Paskorn Champrasert
Teerawat Kumrai
Authors: Paskorn Champrasert
Teerawat Kumrai
Keywords: Computer Science
Issue Date: 28-Oct-2013
Abstract: 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885973382&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/52428
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.