Grey Wolf Optimizer

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on "39 bus IEEE test systems", the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

This study presents a comprehensive and through summary of the Grey Wolf Optimizer (GWO).The GWO algorithm is a newly-presented meta-heuristic, propelled from the social hunting behavior of grey wolves. The GWO has become a progressively critical device of Swarm Intelligence that has been used in nearly all zones of optimization, and engineering practice. Numerous issues from different regions have been effectively illuminated utilizing the GWO algorithm and its variants. In arrange to utilize the calculation to illuminate assorted issues, the original GWO algorithm required to be modified or hybridized. This study conducts an exhaustive review of this living and advancing area of Swarm Intelligence, so that to show that the GWO algorithm might be connected to each issue emerging in hone. However, it empowers novice researchers and algorithm developers to utilize this straightforward and however exceptionally effective algorithm for issue tackling. It frequently ensures that the gained results about will meet the expectation.

Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.

On line tuning of FACTS based damping controller is a vital decisive task in power system. In this regard two things need to be addressed, one is selection of a proper controller and another one is selection of a powerful optimization technique. In this work Grey Wolf Optimizer (GWO) technique is proposed to tune parameters of PI and lead lag controller based on UPFC to damp intra plant and inter area electromechanical oscillations with single and multi machine power system. A broad comparison has been performed with eigen value analysis between optimized PI and lead lag damping controller subject to different disturbances in power system. The recently revealed GWO, standard PSO and DE techniques are explicitly employed to tune UPFC based PI and lead-lag controller parameters. The system response predicts that performance of GWO is much better than PSO and DE techniques, and also lead lag controller is a better choice than PI controller pertaining to design of UPFC based damping controller.

A scheduling algorithm in cloud computing environment is in charge of assigning tasks of a workflow to cloud's virtual machines (VMs) so that the workflow completion time is minimized. Due to the heterogeneity and dynamicity of VMs an d diversity of tasks size, workflow scheduling is confronted with a huge permutation space and is known as an NP-complete problem; therefore, heuristic algorithms are used to reach an optimal scheduling. While the single-objective optimization i.e., minimizing completion time, proposes the workflow scheduling as a NP-complete problem, multi-objective optimization for the scheduling problem is confronted with a more permutation space. In our pre vious work, we considered single-objective optimization (minimizing the workflow compl etion time) using Particle S warm Optimization (PS O) algorithm. The current study aims to present a multi-objective optimizer for conflicting objectives using Gray Wolves Optimizer (GWO) where dependencies exist between workflow tasks. We applied our method to Epigenomics (balanced) and Montage (imbalanced) workflows and compared our results with those of the S PEA2 algorithm based on parameters of Attention Quotient, Max Extension, and Remoteness Dispersal.

This paper introduces modelling and simulation of Doubly-Fed Induction Generator (DFIG) of Wind Energy Conversion System (WECS). Two Pulse Width Modulation (PWM) converters have been connected back to back from the rotor terminals to the utility grid via a dc-link. Vector control system typically controlled by a set of PI controllers, which have an important effect on the performance of system dynamics. This paper presents an optimally tuned PI controllers design of a DFIG wind energy system connected to grid using Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). PSO and GWO used to optimize PI controller parameters of both Grid side converter (GSC), and Rotor side converter (RSC) to improve the dynamic operation of the DFIG wind energy system under a variable speed condition. Keywords-Doubly fed induction generator (DFIG), wind energy conversion system (WECS), Rotor side converter (RSC), Grid side converter (GSC), Particle Swarm Optimization (PSO), Grey Wolf Optimi...

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

Wind energy is one of the world’s leading promising renewable energy sources, due to that there is a prediction that wind generation systems will provide maximum power supply and have good integration with the electric grid. To fulfill the increasing power demand, wind power generation systems need more advanced, novel, and robust control approaches to achieve a more stable operation of the controller and to improve the overall efficiency of the system. This paper presents an optimal design and tuning of fuzzy logic controllers (FLC) for a 1.5-MW doubly fed induction generator (DFIG), grid-connected, wind energy conversion system (WECS) using intelligent methodologies such as particle swarm optimizer (PSO), the gray wolf optimization (GWO), moth-flame optimizer (MFO), and multi-verse optimizer (MVO). FLC scaling factors are optimized for both dc-link voltage controller and current regulators of the grid-side converter and rotor-side converter of the back to back of DFIG wind turbine...

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier.

The use of authentication systems has increased significantly due to the advancement of technology, greater affordability of devices, increased ease of use, and enhanced functionality. This authentication system help safeguard users’ private personal information. These are a plethora of authentication systems based on variety of inputs such as pins, biometrics, and smart cards. All of these authentication systems experience different threats and attacks e.g. shoulder surfing, surfing, spoofing attacks, etc. One way to checkmate this flaw is to use biometric authentication as this cannot be forged. This project developed a face recognition system using Grey wolf and Back-Propagation Neural Network Algorithm.
Fifty-four facial images consisting of twenty eight angry facial expressions and twenty six smiles facial expressions each were captured with a digital camera. All face images taken have equal uniform illumination conditions and light color background. The images were cropped and pre-processed using histogram equalizer, principal component analysis for feature extraction and grey wolf algorithm for feature selection. Back Propagation Neural Network was used for image classification. The performance metrics used for evaluation were recognition accuracy, recognition time, sensitivity, specificity, false rejection rate, recognition time and receiver operative characteristics.
The experimental results obtained revealed that GWA-BPNN classifier gave 96.23% in terms of recognition accuracies, 4.00% false positive rate, 96.43% sensitivity, 96.00% specificity and 17.67s recognition time.

In present study, structural formation identification of a vaulted roof steel structure is taken as optimization problem. The cost of a steel structure is directly related to the weight of the structure. Weight minimization of the vaulted roof steel structure is considered as objective function of the design problem. The design problem is in-tended to be as realistic as possible. Wind loads and snow loads are calculated in di-rection of TS EN 1991-1-4 and TS EN1991-1-3practice code specifications, respec-tively. And dead loads reobtained in terms of gravity. The structural design constraints of the optimization problem are determined according to American Institute of Steel Construction-Allowable Stress Design (AISC-ASD). In the design, W-shaped steel pro-file sections to be selected for assigning to the structural elements are considered as discrete design variables. Grey Wolf Optimizer (GWO) and Backtracking Search Op-timization (BSO) algorithms that are relatively recent metaheuristic algorithms are utilized as optimizer tools to obtain the minimum weighted structural design. The vaulted roof steel structure is initially modeled in a finite element packaged software (ANSYS Workbench v18.1). Then, using the application programming interface of the software, integration of finite element model with GWO and BSO optimization algo-rithms encoded in Microsoft Visual Basic for Application (MS VBA v7) programming language is provided. Thus, the performances of two new generation optimization algorithms in design optimization of a vaulted roof steel structure are compared and the benefits of the application programming interface are demonstrated.