Using opposition-based learning can improve the performance of particle swarm optimization (PSO) algorithm.However

the current opposition-based learning particle swarm optimization algorithms calculate the opposite solution by using coordinates of the candidate solution

the maximum and the minimum of a population

without making full use of the search experience of the population.A neighborhood centroid opposition-based learning strategy is proposed to improve this issue.First

the neighborhood centroid is used as reference point for the generation of the opposite particle

absorbing the population search experience and maintaining diversity.Second

contraction factor is used to expand the reverse search space

increasing the probability of finding a better solution.Experiments are conducted on typical benchmark functions

CEC'13 test functions and also on a practical engineering optimization problem.The results verify the effectiveness of the neighborhood centroid opposition-based learning and the competitiveness of the NCOPSO.