Customer Churn Modeling via the Grey Wolf Optimizer and Ensemble Neural Networks

Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks

In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the perform...

Grey Wolf Optimizer

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The 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, the three main steps of hunting, searching for prey, enc...

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The desig...

Customer churn prediction by hybrid neural networks

Wind Integrated Thermal Unit Commitment Solution using Grey Wolf Optimizer

Received Dec 24, 2016 Revised Apr 26, 2017 Accepted Jun 14, 2017 The augment of ecological shield and the progressive exhaustion of traditional fossil energy sources have increased the interests in integrating renewable energy sources into existing power system. Wind power is becoming worldwide a significant component of the power generation portfolio. Profuse literatures have been reported for...