Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Fault tolerant deployment and topology control in wireless networks
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Power optimization in fault-tolerant topology control algorithms for wireless multi-hop networks
Proceedings of the 9th annual international conference on Mobile computing and networking
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Topology control in wireless ad hoc and sensor networks
ACM Computing Surveys (CSUR)
Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards
Computer Communications
Energy-aware topology control for wireless sensor networks using memetic algorithms
Computer Communications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Computers & Mathematics with Applications
Computers & Mathematics with Applications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Computer Networks: The International Journal of Computer and Telecommunications Networking
Virtual position based geographic routing for wireless sensor networks
Computer Communications
Multiobjective K-connected deployment and power assignment in WSNs using constraint handling
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Lifetime maximization of sensor networks under connectivity and k-coverage constraints
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
On the deployment of wireless data back-haul networks
IEEE Transactions on Wireless Communications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Optimal deployment of large wireless sensor networks
IEEE Transactions on Information Theory
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
Relay node positioning in wireless sensor networks by means of evolutionary techniques
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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The K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D's efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.