ZASTOSOWANIE ROZMYTEJ MAPY KOGNITYWNEJ W PROGNOZOWANIU EFEKTYWNOŚCI PRACY WYPOŻYCZALNI ROWEROWYCH

This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.W pracy zaproponowano zastosowanie rozmytej mapy kognitywnej wraz z ewolucyjnymi algorytmami uczenia do modelowania systemu prognozowania efektywności pracy wypożyczalni rowerowych. Na podstawie danych historycznych zbudowano rozmytą mapę kognitywną, którą następnie zastosowano do prognozowania liczby rowerzystów i klientów wypożyczalni w trzech kolejnych dniach. Proces uczenia zrealizowano z zastosowaniem indywidualnego kierunkowego algorytmu ewolucyjnego IDEA oraz algorytmu genetycznego z kodowaniem zmiennoprzecinkowym RCGA. Analizę symulacyjną systemu prognozowania efektywności pracy wypożyczalni rowerowych przeprowadzono przy pomocy oprogramowania opracowanego w technologii JAVA

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

ROZMYTA MAPA KOGNITYWNA JAKO INTELIGENTNY SYSTEM REKOMENDACYJNY ZASOBÓW STRONY INTERNETOWEJ

This paper is devoted to the construction and analysis of the intelligent recommendation system for website resources based on fuzzy cognitive map. The developed system allows to identify resources, which may be interested in a potential user. These resources are determined on the basis of website users activity. Fuzzy cognitive map was develop using the dataset with anonymous collected historical data. The concepts of fuzzy cognitive map are identifiers of resources of website. Weights of the connection between them have been established based on the number of users visiting the resources.Artykuł poświęcony jest budowie i analizie inteligentnego systemu rekomendacyjnego zasobów bazującego na rozmytej mapie kognitywnej. Opracowany system pozwala wskazać zasoby strony internetowej, którymi może być zainteresowany potencjalny użytkownik. Zasoby te są określane na podstawie aktywności innych użytkowników serwisu. Bazując na zbiorze anonimowo zebranych danych historycznych opracowano rozmytą mapę kognitywną, której czynniki odpowiadają poszczególnym zasobom strony internetowej. Wagi powiązań między nimi określono na podstawie liczby użytkowników odwiedzających poszczególne zasoby

Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio

A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.Comment: 48 pages, 14 Tables, 12 Figure

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace