scholarly journals Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

2021 ◽  
Author(s):  
Jialiang Xie ◽  
Shanli Zhang ◽  
Honghui Wang ◽  
Dongrui Wu
Keyword(s):  
Evolutionary Algorithms ◽  
Fuzzy Inference ◽  
Structural Information ◽  
Test Problems ◽  
Self Organizing Map ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Inference System ◽  
Multi Objective ◽  
Self Organizing

Abstract Two goals of multi-objective evolutionary algorithms are effffectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the difffferential operator, to generate a new initial solution, and the polynomial mutation operator to refifine them. Boundary processing is then conducted. Experiments on fififteen test problems were conducted to verify its effffectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms.

scholarly journals Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 289 ◽  
Author(s):  
Majid Dehghani ◽  
Hossein Riahi-Madvar ◽  
Farhad Hooshyaripor ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Coupled Model ◽  
Grey Wolf ◽  
Hydropower Generation ◽  
Grey Wolf Optimization ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Dam Reservoirs ◽  
Thiessen Polygons

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

scholarly journals HYBRID MODEL OF SELF-ORGANIZING MAP AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN STOCK INDEXES FORECASTING

2021 ◽  
Vol 9 (2) ◽  
pp. 70-80
Author(s):  
M. Kushnir ◽  
K. Tokarieva
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Prices ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Financial Time Series ◽  
Self Organizing Map ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Self Organizing

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. It is uncovered that scholars and practitioners face some difficulties in modelling complex system such as the stock market because it is nonlinear, chaotic, multi- dimensional, and spatial in nature, making forecasting a complex process. Models estimating nonstationary financial time series may include noise and errors. The relationship between the input and output parameters of the models is essentially non-linear, where stock prices include higher-level variables, which complicates stock market modeling and forecasting. It is also revealed that financial time series are multidimensional and they are influenced by many factors, such as economics, politics, environment and so on. Analysis and evaluation of multi- dimensional systems and their forecasting should be carried out by machine learning models. The problem of forecasting the stock market and obtaining quality forecasts is an urgent task, and the methods and models of machine learning should be the main mathematical tools in solving the above problems. First, we proposed to use self-organizing map, which is used to visualize multidimensional data by configuring neurons to quantize or cluster the input space in the topological structure. These characteristics of this algorithm make it attractive in solving many problems, including clustering, especially for forecasting stock prices. In addition, the methods discussed, encourage us to apply this cluster approach to present a different data structure for forecasting. Thus, models of adaptive neuro-fuzzy inference system combine the characteristics of both neural networks and fuzzy logic. Given the fact that the rule of hybrid learning and the theory of logic is a clear advantage of adaptive neuro-fuzzy inference system, which has computational advantages over other methods of parameter identification, we propose a new hybrid algorithm for integrating self-organizing map with adaptive fuzzy inference system to forecast stock index prices. This algorithm is well suited for estimating the relationship between historical prices in stock markets. The proposed hybrid method demonstrated reduced errors and higher overall accuracy.

Multi-objective grey wolf optimization of solar chimneys based on an improved model incorporating a wind turbine power curve

2021 ◽  
Vol 239 ◽  
pp. 114231
Author(s):  
Ali Habibollahzade ◽  
Iman Fakhari ◽  
Saeed Mohsenian ◽  
Hossein Aberoumand ◽  
Robert A. Taylor
Keyword(s):  
Wind Turbine ◽  
Power Curve ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Multi Objective ◽  
Improved Model

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

scholarly journals Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1192
Author(s):  
Randall Claywell ◽  
Laszlo Nadai ◽  
Imre Felde ◽  
Sina Ardabili ◽  
Amirhosein Mosavi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Grey Wolf Optimizer ◽  
Multi Layer Perceptron ◽  
Grey Wolf ◽  
Inference System ◽  
Diffuse Fraction ◽  
Testing Procedures

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.

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