Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Energy-efficient coverage problems in wireless ad-hoc sensor networks
Computer Communications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Pseudocoevolutionary genetic algorithms for power electronic circuits optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Visual sensor network lifetime maximization by prioritized scheduling of nodes
Journal of Network and Computer Applications
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The SET k-cover problem is an NP-complete combinatorial optimization problem, which is derived from constructing energy efficient wireless sensor networks (WSNs). The goal of the problem is to find a way to divide sensors into disjoint cover sets, with every cover set being able to fully cover an area and the number of cover sets maximized. Instead of using deterministic algorithms or simple genetic algorithms (GAs), this paper presents a hybrid approach of a GA and a stochastic search. This approach comprises two core modules. The first is the interaction module, which is applied to improve the quality of the population through interaction of individuals. The second is the self construction module, which is a stochastic search procedure running without interaction of individuals. The interaction module is implemented as a combination of selection and crossover, which can efficiently exploit the solutions currently found. The self-construction module includes an adjusted mutation operation and three additional operations. This module is the main force to explore the solution space which can eliminate the inefficiency of using classical GA operations to explore the solution space. Experimental results show that the propose algorithm performs better than the other existing approaches.