中文说明：粒子群优化算法 (PSO) 是进化计算由坚尼地介绍该地区的一个新分支 和埃伯哈特在 1995 年 [8]。PSO 易于实施和经验主义地好上很多优化执行已验证 问题。由于在求解无约束的优化问题的粒子群成功，PSO 也逐渐 在过去几年获得高阶警察的注意。建议动态的多群梁和 Suganthan [9] 一种新型约束处理机制解决警察与 PSO。在其提案中人口是定期 并随机分为几个小组群。此外，目标和约束条件分配给小组群 自适应地根据约束的困难。因此，更难的约束将点火 群为它，并且更容易的约束工作将有更少群为它工作。此外，序贯二次规划 (SQP) 用于本地搜索。Krohling 和 Coelho [10] 提出了基于高斯分布的约束优化，在其中使用高斯概率分布来生成 PSO 的加速系数 co 进化 PSO。此外，两个种群的进化在此方法中： 一个用于变量向量，其他为拉格朗日乘数向量。他和王 [11] 还提议共同进化 PSO。在此方法中，共同进化的观念被受雇以应付决策变量和约束。两个群进化以交互方式使用 PSO、 一个用于搜索很好的解决方案和其他用于优化适当的刑罚因素。最近，他和王 [12] 向介绍混合 PSO 与可行性基于规则 [13]
English Description：
Particle swarm optimization (PSO) is a new branch in the area of evolutionary computation introduced by Kennedy and Eberhart in 1995 [8]. PSO is easy to implement and has been empirically verified to perform well on many optimization problems. Because of the success of PSO in solving unconstrained optimization problems, PSO has gradually gained attention in sloving COPs in the last few years. Liang and Suganthan [9] proposed a dynamic multi-swarm PSO with a novel constraint-handling mechanism for solving COPs. In their proposal, the population is periodically and randomly divided into several sub-swarms. In addition, the objective and constraints are assigned to the sub-swarms adaptively according to the difficulties of the constraints. As a result, the constraints that are more difficult will havemore sub-swarms working for it, and the constraints that are easier will have less sub-swarms working for it. Moreover, sequential quadr