Charging station location and capacity optimization is a critical issue in the field of electric vehicle (EV) infrastructure planning as it involves multiple objectives and constraints. In this case, finding optimal s...
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ISBN:
(纸本)9798350381993;9798350382006
Charging station location and capacity optimization is a critical issue in the field of electric vehicle (EV) infrastructure planning as it involves multiple objectives and constraints. In this case, finding optimal solution for charging station placement problem requires an efficient optimization tool that could find quality solutions in short time. With this in mind, this work proposes a novel binary variant of atomsearchoptimization (ASO) algorithm, utilizing the concept of quantum binarization technique, namely quantum-inspired binary ASO (QBASO) algorithm. The proposed QBASO possesses the same structure as original ASO algorithm, but the binarization procedure is performed using quantum gates and quantum bits. The performance of the proposed QBASO is tested using wellknown benchmark functions and is then applied on EV charging station placement problem. The experimental results and statistical analyses show that QBASO algorithm is a promising optimization technique for solving charging station placement problem.
This article presents a modified version of atomsearchoptimization (ASO) algorithm that uses the opposition-based learning (OBL) to improve the search space exploration. OBL is a commonly used machine learning strat...
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This article presents a modified version of atomsearchoptimization (ASO) algorithm that uses the opposition-based learning (OBL) to improve the search space exploration. OBL is a commonly used machine learning strategy for increasing the performance of meta-heuristic algorithms. As a new design method, the opposition-based ASO (OBASO) algorithm was proposed for the first time in determining the optimum values of the proportional-integral-derivative plus second order derivative (PIDD2) controller parameters in an automatic voltage regulator (AVR) system. In the design problem, a new objective function, including the integral of time-weighted squared error (ITSE) and overshoot all together, was optimized with the proposed OBASO algorithm to find the best values of the PIDD2 controller parameters. The performance of the proposed OBASO tuned PIDD2 (OBASO-PIDD2) controller is compared to that of the classic ASO tuned PIDD2 (ASO-PIDD2) controller as well as the PID, fractional order PID (FOPID) and PIDD2 controllers tuned with modern meta-heuristic algorithms. Comparative transient and frequency response analyzes were conducted to assess the stability of the proposed approach. In addition, considering the possible changes in AVR parameters, the robustness of the proposed approach was tested. The extensive simulation results and comparisons with other existing controllers show that the proposed OBASO-PIDD2 controller with a new objective function has a superior control performance and can highly improve the system robustness with respect to model uncertainties.
Filtering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led resear...
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Filtering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led researchers to design filters using emerging evolutionary computations. Metaheuristics have emerged as the most promising tool for solving optimization problems, with excellent development and improvement. However, it has not been clear how to select the best performing metaheuristic to design an optimal digital filter. In this paper, a digital infinite impulse response (IIR) filter is constructed using the atomsearchoptimization (ASO) algorithm impressed by the physical motion of atoms in nature based on molecular dynamics. The simulation results obtained are extensively compared with the results of other optimizationalgorithms such as moth flame optimization, gravitational searchalgorithm and artificial bee colony optimization. ASO was found to have the highest percentage of improvement. Furthermore, eight cases are analyzed across four numerical filter instances with the same degree and four with reduced degree, and the results are validated by outperforming several different algorithm-based approaches in the literature. The stability analysis on the basis of pole zero diagrams further cements the efficacy of the ASO for IIR system identification problem.
Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients' risk factors and assuring accurate diagnosis. This work proposes a clinical decision ...
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Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients' risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the atom search optimization algorithm and the Henry Gas Solubility optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew's correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor.
Security has become crucial as Internet of Things (IoT) applications have grown in popularity. The prevalence of IoT vulnerabilities was recently exposed by a distributed denial-of-service (DDoS) assault, and many IoT...
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The tourism industry has been integrated into the national strategic system in China. Thus, tourism demand forecasting has become a concern for the sustainable development of the tourism industry. Unfortunately, the s...
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The tourism industry has been integrated into the national strategic system in China. Thus, tourism demand forecasting has become a concern for the sustainable development of the tourism industry. Unfortunately, the sample size for tourism in China is always small and cannot satisfy the hypothesis test of an economic model or the data volume for a traditional time series model. In this study, a novel hybrid forecasting framework combining fuzzy time series (FTS) and an atomsearchoptimization (ASO) algorithm is proposed for inbound tourism demand forecasting;this forecasting framework is particularly suitable for small sample sizes. Specifically, information optimization technology is applied in the FTS to improve the recognition ability of the system and effectively identify small sample information. The ASO algorithm is applied to search the optimal parameters of FTS that can further improve forecasting performance. All comparison experiments and tests verify the effectiveness and superiority of our proposed model, which provides excellent forecasting results for tourism demand and a basis for policymakers and managers to plan appropriately for the tourism market. (C) 2020 Elsevier B.V. All rights reserved.
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