Abstract
According to the function of wind speed and wind power made by least square regression, we use genetic algorithm (GA) for one-dimensional test. The meanwhile, based on the impact of wind speed and temperature relation in two-dimensional function, we apply niche genetic algorithm (NGA) for training. We use eight combinations: elitist, adaptive crossover and mutation probability; niche algorithm; pre-selection algorithm; penalty function algorithm; niche algorithm and pre-selection algorithm combination; niche algorithm and penalty function; pre-selection algorithm and penalty function algorithm; niche algorithm, pre-selection algorithm and penalty function algorithm for joint use. In comparison, the niche algorithm, pre-selection algorithm, genetic algorithm penalty function used in conjunction iteration is better. The average fitness of two-dimensional niche genetic algorithm iteration is smooth and basically stable. One-dimensional NGA fitness situation: fval =85.4512. The elapsed time is 8.073602s. The final fitness value of two-dimensional fitness function is 749.9563. (5 pages)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have