Measurement of Population Diversity
Selected Papers from the 5th European Conference on Artificial Evolution
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Eigenvalue-based spectrum sensing algorithms for cognitive radio
IEEE Transactions on Communications
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Spectrum sensing measurements of pilot, energy, and collaborative detection
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Cooperative sensing via sequential detection
IEEE Transactions on Signal Processing
IIR system identification using cat swarm optimization
Expert Systems with Applications: An International Journal
Solving multiobjective problems using cat swarm optimization
Expert Systems with Applications: An International Journal
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user's signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.