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 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.