中文摘要：

针对反向粒子群优化算法存在的易陷入局部最优、计算开销大等问题,提出了一种带自适应精英粒子变异及非线性惯性权重的反向粒子群优化算法（OPSO-AEM＆NIW）,来克服该算法的不足。OPSO-AEM＆NIW算法在一般性反向学习方法的基础上,利用粒子适应度比重等信息,引入了非线性的自适应惯性权重（NIW）调整各个粒子的活跃程度,继而加速算法的收敛过程。为避免粒子陷入局部最优解而导致搜索停滞现象的发生,提出了自适应精英变异策略（AEM）来增大搜索范围,结合精英粒子的反向搜索能力,达到跳出局部最优解的目的。上述2种机制的结合,可以有效克服反向粒子群算法的探索与开发的矛盾。实验结果表明,与主流反向粒子群优化算法相比,OPSO-AEM＆NIW算法无论是在计算精度还是计算开销上均具有较强的竞争能力。

英文摘要：

An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight（OPSO-AEMNIW） was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introduced to balance the contradiction between exploration and exploitation during its iterations process. The first one was nonlinear adaptive inertia weight（NIW）, which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each particle using relative information such as particle fitness proportion. The second one was adaptive elite mutation strategy（AEM）, which aim to avoid algorithm trap into local optimum by trigging particle＇s activity. Experimental results show OPSO-AEMNIW algorithm has stronger competitive ability compared with opposition-based particle swarm optimizations and its varieties in both calculation accuracy and computation cost.