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

• Main Authors: Paskorn Champrasert, Teerawat Kumrai
• Format: Conference Proceeding
• Published: 2018
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84885973382&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/52428
• Institution: Chiang Mai University

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.