The Grey Wolf Optimizer(GWO) algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition,
In the Multi-Objective Grey Wolf Optimizer (MOGWO), a fixed-sized external archive is integrated to the GWO for saving and retrieving the Pareto optimal solutions. This archive has been employed to define the social hierarchy and simulate the huntin
Soft computing and nature-inspired computing both play a significant role in developing a better understanding to machine learning. When studied together, they can offer new perspectives on the learning process of machines. The Handbook of Research
# EvoloPy: An open source nature-inspired optimization toolbox for global optimization in Python
The EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization. The list of optimizers that have been imple
为获得微电网系统建设成本、环境成本和运行成本的多重目标优化，以构建系统独立运行模块和仿真模块为核心，设计了微电网系统的多目标调度模型。使用能量模块对微电网调度模型的建设成本、环境成本和运行成本指标进行评价，优化调度算法模块则使用基于个体密度多目标狼群算法(Multi-objective wolf colony algorithm，MOWCA)。在MOWCA算法中引入了非支配排序和个体密度多样性保持操作，有效提高了多目标优化的前沿分布多样性和收敛精度。将所提优化调度算法基于Docker容器技术，对
