中文摘要：

为解决反向粒子群优化算法计算开销大、易陷入局部最优的不足,提出一种无惯性的自适应精英变异反 向粒子群优化算法（ NOPSO）. NOPSO算法在反向学习方法的基础上,广泛获取环境信息,提出一种无惯性的速 度（ NIV）更新式来引导粒子飞行轨迹,从而有效加快算法的收敛过程.同时,为避免早熟现象的发生,引入了 自适应精英变异策略（ AEM）,该策略在扩大种群搜索范围的同时,帮助粒子跳出局部最优.N IV与 A EM这 2 种机制的结合,有效增加了种群多样性,平衡了反向粒子群算法中探索与开发的矛盾.实验结果表明,与主流反 向粒子群优化算法相比,NOPSO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：

b Non-inertial opposition-based particle swarm optimization with adaptive elite mutation （NOPSO） was pro-posed to overcome the drawbacks, such as, slow convergence speed, falling into local optimization, of opposition-based particle swarm optimization. In addition to increasing the diversity of population, two mechanisms were introduced to balance the contradiction between exploration and exploitation during its iterations process. The first one was non-inertial velocity （NIV） equation, which aimed to accelerate the process of convergence of the algorithm via better access to and use of environmental information. The second one was adaptive elite mutation strategy （AEM）, which aimed to avoid trap into local optimum. Experimental results show NOPSO algorithm has stronger competitive ability compared with opposition-based particle swarm optimizations and its varieties in both calculation accuracy and computation cost.