一种邻域重心反向学习的粒子群优化算法

粒子群优化算法使用反向学习技术可以提高性能.然而，现有的反向学习粒子群优化算法仅采用粒子最大最小边界计算反向解，没有充分利用群体搜索经验.针对此问题，提出了一种邻域重心反向学习策略，使用邻域重心作为参考点计算反向解，充分吸收群体搜索经验的同时保持种群多样性；采用收缩因子拓展反向解搜索范围，增加找到更高质量解的机率.在典型的基准测试函数、CEC'13测试函数和一个实际工程优化问题上进行验证，实验结果说明了邻域重心反向学习策略的有效性和本文算法的竞争力.

Neighborhood Centroid Opposition-Based Particle Swarm Optimization

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.