基于子区域的粒子群优化算法研究_曾嘉俊

新型的智能优化算法，水波优化算法，在基准函数CEC2014上比PSO，BBO，HS等表现要好。(A New Intelligent Optimum Algorithms, Water Wave Optimum Algorithms)

说明：  原始的最优化算法，用来求解最小值最大值，适合新手入门学习(fruit fly optimization)

minmin算法，适用于任务调度，可以与GA以及PSO等进行比较，是最基础的算法。(Minmin algorithm is suitable for task scheduling.)

说明：  构建基于改进灰狼优化算法的神经网络数据预测模型(IGWO-BPNN)，目的在于用改进的灰狼优化算法优化神经网络模型，利用神经网络的反向传播优势，改善神经网络算法易于陷入局部最小值的缺陷，提高神经网络模型的预测精度。(The grey wolf algorithm (GWO), which is inspired by the predatory behavior of the gray wolf group, is a new group intelligent optimization algorithm that imitates the leadership of gray wolf population and hunting mechanism in nature)

通过群算法（BSO和CSO），实现寻优的功能。(Through group algorithm (BSO and CSO), the optimization function is realized.)

求解多目标问题，业内最经典的多目标算法之一(to solve multi-object problem)

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说明：  含电动汽车的微电网的优化运行，用粒子群算法进行多目标优化(Multi-objective optimization is carried out by using particle swarm optimization algorithm (PSO) to optimize the operation of microgrid with electric vehicles)

说明：  粒子群算法的寻优机制，另附十余个测试函数。主程序为test_basic(Particle Optimization Algorithm)

多个萤火虫算法基础算例，为萤火虫算法通用算例(benchmark problem for firefly algorithm)