Optimization is a broad field for researchers to develop new algorithms solving various types of problems. There are popular techniques being worked on improvement. Grey wolf optimization (GWO) one such algorithm because it efficient, simple use, and easy implement. However, GWO has several drawbacks as stuck in local optima, low convergence rate, poor exploration. Several attempts have been ma...

A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) attitude (roll, pitch, yaw) control of a quadrotor vehicle. The derivation mathematical model the formulated based on Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain superior performance. Therefore, we exploit advantages nature-inspired...

This article introduces a novel nature-inspired algorithm called the Plum Tree Algorithm (PTA), which has biology of plum trees as its main source inspiration. The PTA was tested and validated using 24 benchmark objective functions, it further applied compared to following selection representative state-of-the-art, algorithms: Chicken Swarm Optimization (CSO) algorithm, Particle (PSO) Grey Wolf...

A partial shading condition (PSC) is one of the most common problems in photovoltaic (PV) system. It causes output power a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track maximum point power-voltage curve with multiple peaks. Grey wolf optimization (GWO) algorithm new based on MHA. has been used to solve many applications including MPPT for However, accuracy and tracki...

The design of advanced robust control is crucial for serial robotic manipulators under various uncertainties and disturbances in case the forceful performance needs industrial applications. Therefore, this work has proposed implementation a fractional order proportional tilt integral derivative (FOPTID) controller joint space 3-DOF manipulator. been designed based on calculus concept to fulfill...

Abstract Considering the complexities and challenges in classification of multiclass imbalanced fault conditions, this study explores systematic combination unsupervised supervised learning by hybridising clustering (CLUST) optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset f...

The goal of this article is to use MPPTs (maximum power point trackers) extort maximum from best configuration or combine renewable resources and energy storage systems that all work together in off-grid for electric vehicle charging. grey wolf algorithm (GWO) searches the MPP at partial shading condition (PSC) with following two consideration one high oscillations around GMPPs, other they are ...

Automatic programming (AP) is an important area of Machine Learning (ML) where computer programs are generated automatically. Swarm Programming (SP), a newly emerging research in AP, automatically generates the using Intelligence (SI) algorithms. This paper presents two grammar-based SP methods named as Grammatical Moth-Flame Optimizer (GMFO) and Whale (GWO). The Optimization algorithm used sea...

The likelihood of surface water and groundwater contamination is higher in regions close to landfills due the possibility leachate percolation, which a potential source pollution. Therefore, proposing reliable framework for monitoring parameters an essential task managers authorities quality control. For this purpose, efficient hybrid artificial intelligence model based on grey wolf metaheurist...

In this work, we have developed two new intelligent maximum power point tracking (MPPT) techniques for photovoltaic (PV) solar systems. To optimize the PWM duty cycle driving DC/DC boost converter, used optimization algorithms namely whale algorithm (WOA) and grey wolf (GWO) so can tune PID controller gains. The oscillation around MPP fail accuracy under fast variable isolation are among well-k...