1,315 research outputs found

    Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

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    Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit

    A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

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    The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

    Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem

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    Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features

    A hybrid approach of anfis—artificial bee colony algorithm for intelligent modeling and optimization of plasma arc cutting on monel™ 400 alloy

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    This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes

    A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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    Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

    Scientific research trends about metaheuristics in process optimization and case study using the desirability function

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    This study aimed to identify the research gaps in Metaheuristics, taking into account the publications entered in a database in 2015 and to present a case study of a company in the Sul Fluminense region using the Desirability function. To achieve this goal, applied research of exploratory nature and qualitative approach was carried out, as well as another of quantitative nature. As method and technical procedures were the bibliographical research, some literature review, and an adopted case study respectively. As a contribution of this research, the holistic view of opportunities to carry out new investigations on the theme in question is pointed out. It is noteworthy that the identified study gaps after the research were prioritized and discriminated, highlighting the importance of the viability of metaheuristic algorithms, as well as their benefits for process optimization

    An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

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    [EN] Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.This work was supported in part by the National Research Foundation of Korea Grant Funded by the Korea Government (MSIT) under Grant 2019M3F2A1073179; in part by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" Within the Project under Grant TIN2017-84802-C2-1-P; and in part by the European Union through the ERANETMED (Euromediterranean Cooperation through ERANET Joint Activities and Beyond) Project ERANETMED3-227 SMARTWATIR.Han, T.; Muhammad, K.; Hussain, T.; Lloret, J.; Baik, SW. (2021). An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things. 8(5):3170-3179. https://doi.org/10.1109/JIOT.2020.3013306S317031798
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