Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

A survey of teaching–learning-based optimization

Accurate crop type mapping provides numerous benefits for a deeper understanding of food systems and yield prediction. Ever-increasing big data, easy access to high-resolution imagery, and cloud-based analytics platforms like Google Earth Engine have drastically improved the ability for scientists to advance data-driven agriculture with improved algorithms for crop type mapping using remote sensing, computer vision, and machine learning. Crop type mapping techniques mainly relied on standalone SAR and optical imagery, few studies investigated the potential of SAR-optical data fusion, coupled with virtual constellation, and 3-dimensional (3D) deep learning networks. To this extent, we use a deep learning approach that utilizes the denoised backscatter and texture information from multi-temporal Sentinel-1 SAR data and the spectral information from multi-temporal optical Sentinel-2 data for mapping ten different crop types, as well as water, soil and urban area. Multi-temporal Sentinel-1 data was fused with multi-temporal optical Sentinel-2 data in an effort to improve classification accuracies for crop types. We compared the results of the 3D U-Net to the state-of-the-art deep learning networks, including SegNet and 2D U-Net, as well as commonly used machine learning method such as Random Forest. The results showed (1) fusing multi-temporal SAR and optical data yields higher training overall accuracies (OA) (3D U-Net 0.992, 2D U-Net 0.943, SegNet 0.871) and testing OA (3D U-Net 0.941, 2D U-Net 0.847, SegNet 0.643) for crop type mapping compared to standalone multi-temporal SAR or optical data (2) optical data fused with denoised SAR data via a denoising convolution neural network (OA 0.912) performed better for crop type mapping compared to optical data fused with boxcar (OA 0.880), Lee (OA 0.881), and median (OA 0.887) filtered SAR data and (3) 3D convolutional neural networks perform better than 2D convolutional neural networks for crop type mapping (SAR OA 0.912, optical OA 0.937, fused OA 0.992).

Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine

A novel design method, sine-cosine algorithm (SCA) is presented in this paper to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AV...

Sine-cosine algorithm-based optimization for automatic voltage regulator system:

Today, finding a viable solution for any real world problem focusing on combinatorial of problems is a crucial task. However, using optimisation techniques, a viable best solution for a specific problem can be obtained, developed and solved despite the existing limitations of the implemented technique. Furthermore, population based optimisation techniques are now a current interest and has spawned many new and improved techniques to rectify many engineering problems. One of these methods is the Grey Wolf Optimiser (GWO), which resembles the grey wolf’s leadership hierarchy and its hunting behavior in nature. The GWO adopts the hierarchical nature of grey wolfs and lists the best solution as alpha, followed by beta and delta in descending order. Additionally, its hunting technique of tracking, encircling and attacking are also modeled mathematically to find the best optimised solution. This paper presents the results from an extensive study of 83 published papers from previous studies related to GWO in various applications such as parameter tuning, economy dispatch problem, and cost estimating to name a few. A discussion on the properties of GWO algorithm and how it minimises the different problems in the different applications is presented, as well as an analysis on the research trend of GWO optimisation technique in various applications from year 2014 to 2017. Based on the literatures, it was observed that GWO has the ability to solve single and multi-objective problems efficiently due to its good local search criteria that performs exceptionally well for different problems and solutions.

Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017)

This paper presents a novel evolutionary technique to optimize the parameters of fractional order controller for controlling two classes of systems: time-delay and higher-order system. The evolutionary technique known as grey wolf optimizer is used to tune both integer and fractional order controllers. The grey wolf optimizer searches for the optimum solution in the following manner, i.e. encircling, hunting, attacking the prey and finally search for the new prey consecutively if exists. To certify these various procedure, the performance indices like integral square error, integral absolute error, integral time-weighted square error, and integral time-weighted absolute error are minimized for the authenticity. Moreover, the proposed algorithm is validated and compared with well-established techniques.

Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer

Optimized PID Controller for Magnetic Levitation System

This paper demonstrates the potency of evolution based optimization techniques in the sense of enhancing the system’s performance. Teaching Learning Based Optimization (TLBO) is a well-known evolutionary algorithm used to optimize the parameters of the PID controller so as to improve the performance of the magnetic levitation system. The TLBO search algorithm is split into two phases, the teacher phase and the learner phase. The teacher phase is comprised of having minimum performance index as compared to learner phase. The learners improve their knowledge on the basis of teacher’s performance. The parameters are tuned while minimizing the performance index of the system. The performance index incorporated in this paper is the integral time weighted square error (ITSE). The corroboration of the above technique is ended by comparing it with the conventional control techniques.

Performance enhancement of magnetic levitation system using teaching learning based optimization

paper deals with the control aspects of magnetic levitation system using fuzzy logic controller (FLC). The magnetic levitation system is a mechatronic system already acknowledged and accepted by the field experts. For such a system it is desired to proposed a suitable controller for positioning a metal sphere in air space by the help of an electromagnetic force. In the ideal situation, the magnetic force produced by current from an electromagnet counteracts the weight of the metal sphere. Nevertheless, the electromagnetic force is very sensitive, and there is noise that creates acceleration forces on the metal sphere, causing the sphere to move into the unbalanced region. FLC proposed in this paper is to control the nonlinear magnetic ball suspension system using Mamdani implication method. Samples of simulation results with different initial conditions concerning the ball's position and speed are provided included to validate the theory. The effectiveness of this proposed technique is validated through experimental results obtained by performing experiments on Feedback make Magnetic levitation system (33-210) Keywordslevitation system, Fuzzy logic control, PID control

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Digital Control of Magnetic Levitation System using Fuzzy Logic Controller

In this paper an Optimal Fractional Order proportional-integral-derivative (FOPID) controller is designed to levitate the iron ball in the air space using an electromagnetic coil. The infra-red sensor fixed on the vertical sides of the magnetic levitation system (MLS) is being helpful to sense the position of the ball. The controller enables the electromagnetic coil to maintain a specified force so that the ball is levitated. To achieve the desired electro-magnetic force the performance index i.e. the integral square error (ISE) of the system is minimized. The parameters of FOPID controller are optimized using Nelder's-Mead (NM) optimization algorithm. The integer order approximation of the FOPID controller is done by using Oustaloup's method. The performance of the proposed controller is validated by comparing it with the conventional PID controller.

Shekhar Yadav

Papers

Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer

Optimized PID Controller for Magnetic Levitation System

Performance enhancement of magnetic levitation system using teaching learning based optimization

Digital Control of Magnetic Levitation System using Fuzzy Logic Controller

Optimal fractional order PID controller for magnetic levitation system