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Gray wolf optimization algorithm based on differentialevolution and survival of fitness strategy(PDF)


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Gray wolf optimization algorithm based on differentialevolution and survival of fitness strategy
Zhu HaiboZhang Yong
School of Electronics and Information Engineering,University of Science andTechnology Liaoning,Anshan 114051,China
differential evolution elimination strategy gray wolf optimization algorithm typical unimodal or multimodal benchmarks functions
An improved gray wolf optimization algorithm is proposed for the slow convergence speed,low optimization accuracy and premature convergence of the gray wolf optimization algorithm. Based on the basic gray wolf optimization algorithm,a population of individual variation is generated by the differential evolution mechanism,and the local optimization is avoided by a dynamic scaling factor and a crossover probability factor. After variation,m wolfs are eliminated by comparing the individual fitness value,and the same number of wolves are randomly generated based on survival of the fitness. This algorithm is tested by typical unimodal or multimodal benchmarks functions. The simulation results show that the optimization performance of this algorithm is better than that of the particle swarm optimization(PSO)and artificial bee colony(ABC)algorithm etc.,and the efficiency and accuracy of local search are improved. This algorithm is applied to the optimization of the actual control parameters of a condenser and compared with the PSO,genetic algorithm(GA)and engineering turning(ZN)methods. The simulation results show that the adjustment time and rise time of the output response for the adjusted parameter are reduced,the maximum overshoot is reduced,and the stability is good.


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Last Update: 2018-12-30