scholarly journals Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 901
Author(s):  
Medine Colak ◽  
Mehmet Yesilbudak ◽  
Ramazan Bayindir
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Multilayer Perceptron ◽  
Grey Wolf ◽  
Activation Functions ◽  
Power Prediction ◽  
Grey Wolf Optimization ◽  
Photovoltaic Power ◽  
Whale Optimization ◽  
Ant Lion

Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.

scholarly journals Feature selection algorithm for usability engineering: a nature inspired approach

2021 ◽  
Author(s):  
Rajat Jain ◽  
Tania Joseph ◽  
Anvita Saxena ◽  
Deepak Gupta ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Evaluation Method ◽  
Bat Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
User Friendliness ◽  
Software Usability ◽  
Software Model ◽  
Whale Optimization ◽  
Selection Of

AbstractSoftware usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

Feature Selection Method based on Grey Wolf Optimization and Simulated Annealing

2019 ◽  
Vol 12 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Optimization Method ◽  
Selection Method ◽  
Attribute Selection ◽  
Binary Particle Swarm Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Whale Optimization

Feature selection sometimes also known as attribute subset selection is a process in which optimal subset of features are elected with respect to target data by reducing dimensionality and removing irrelevant data. There will be 2^n possible solutions for a dataset having n number of features which is difficult to solve by conventional attribute selection method. In such cases metaheuristic-based methods generally outruns the conventional methods. Therefore, this paper introduces a binary metaheuristic feature selection method bGWOSA which is based on grey wolf optimization and simulated annealing. The proposed feature selection method uses simulated annealing for enhancing the exploitation rate of grey wolf optimization method. The performance of the proposed binary feature selection method has been examined on the ten feature selection benchmark datasets taken from UCI repository and compared with binary cuckoo search, binary particle swarm optimization, binary grey wolf optimization, binary bat algorithm and binary hybrid whale optimization method. Statistical analysis and Experimental results validate the efficacy of proposed method.

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

scholarly journals Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 109 ◽  
Author(s):  
Jiale Ding ◽  
Guochu Chen ◽  
Kuo Yuan
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Percentage Error ◽  
Grey Wolf ◽  
Power Prediction ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Wind Power Prediction ◽  
Learning Machine

In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM, back propagation (BP), and the improved GWO-ELM model. The simulation results show that the proposed model has higher prediction accuracy than other models in short-term wind power prediction.

scholarly journals Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6794
Author(s):  
Tomasz Krzeszowski ◽  
Krzysztof Wiktorowicz
Keyword(s):  
Discriminant Analysis ◽  
Swarm Intelligence ◽  
Hybrid Methods ◽  
Gait Recognition ◽  
Classification Methods ◽  
Grey Wolf ◽  
New Classification ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept.

scholarly journals Performance and evaluation of energy optimization techniques for wireless body area networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Naveen Bilandi ◽  
Harsh Kumar Verma ◽  
Renu Dhir
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Relay Node ◽  
Body Area Networks ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Body Area ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

Abstract Background Wireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem. Results It has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization. Conclusions The results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results. Graphical Abstract

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