Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: A survey

One of the important features of the cognitive radio engine is to adapt the parameters of radio to fulfill certain objectives in a time varying wireless environment. In order to achieve this adaptation, six evolutionary algorithms are employed for optimizing the predefined fitness functions in the radio environment. The performance of genetic algorithm, particle swarm optimization, differential evolution, bacterial foraging optimization, artificial bee colony optimization and cat swarm optimization algorithm in different modes of operation are studied in detail. Each algorithm is tested in single and multicarrier communication system in order to acknowledge the advantage of multicarrier communication systems in wireless environment. The spectral interference introduced by the cognitive user into the primary user’s band and that introduced by the primary user into the cognitive user’s band are also investigated. The performance of different algorithms are compared using convergence characteristics and four statistical metrics.