Improved Cuckoo Search Algorithm and Its Application in WSN
An improved cuckoo search algorithm is proposed to address the defects of slow convergence speed and easy trapping into local optima in the original cuckoo search algorithm.The improved algorithm uses roulette wheel to select the bird's nest and elite guide Levy to fly for updating the bird's nest position in order to accelerate the convergence speed of the algorithm.Additionally,the migration rate and emigration rate models from the biogeography-based algorithm are introduced to execute the Golden Sine Algorithm with different probabilities.Finally,the nest positions are modified using the differential evolution algorithm to improve the diversity of the population,thus avoiding the algorithm from getting trapped into local optima and enhancing the global search capability.Fifteen benchmark functions and simulation experiments of wireless sensor networks in three different scenarios are conducted to evaluate the effectiveness of the proposed algorithm in comparison with related literature,and the results demonstrate the effectiveness of the proposed algorithm.