Traditional particle swarm optimization(PSO)algorithm tends to suffer from premature convergence;we proposed an elite opposition-based learning strategy in which elite particles are introduced to generate their opposite solutions by opposition-based learning.This mechanism can expand the search area and is helpful to enhance the global explorative ability of PSO.Meanwhile

a differential evolutionary mutation strategy is presented to avoid the best particle being trapped into local optima

since this may cause search stagnation of the whole swarm.This strategy adopts differential evolution algorithm to search for the neighborhoods of the global best particle and is helpful to enhance the exploitation ability of PSO.We compared our algorithm with some state-of-the-art PSOs on 14 benchmarks

the results show that our algorithm obtains better solution accuracy and quicker convergence speed.