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Novel comparative methodology of hybrid support vector machine with meta-heuristic algorithms to develop an integrated candlestick technical analysis model

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Journal of Capital Markets Studies 2024年第1期8卷 67-94页

作者： Mahmoodi, Armin Hashemi, Leila Mahmoodi, Amin Mahmoodi, Benyamin Jasemi, Milad Department of Aerospace Engineering Carleton University Ottawa Canada Department of Mechanical and Aerospace Engineering Carleton University Ottawa Canada Department of Mechanical Engineering Islamic Azad University of Izeh Izeh Iran Department of Industrial Engineering Islamic Azad University of Izeh Izeh Iran Stephens College of Business University of Montevallo Montevallo AL United States

Purpose: The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA). Design/methodology/approach: In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent. Findings: Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models. Research limitations/implications: In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions. Originality/value: In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day

关键词： Candlestick technical analysis Machine learning meta-heuristic algorithms Stock market predicting Support vector machine

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Improved market prediction using meta-heuristic algorithms and time series model and testing market efficiency

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Iran Journal of Computer Science 2023年第1期6卷 29-61页

作者： Shahvaroughi Farahani, Milad Farrokhi-Asl, Hamed Department of Finance Faculty of Management Khatam University Tehran Iran Sheldon B. Lubar College of Business University of Wisconsin-Milwaukee Milwaukee WI United States

This study aims to evaluate the efficiency of using technical indicators such as closing price, lowest price, highest price, and exponential moving average in the prediction of stock prices. We use a genetic algorithm (GA) and a hybrid of grey wolf optimization and particle swarm optimization binary algorithm (GWO-PSO) as feature selection methods. In addition, we train our neural network by using some metaheuristic algorithms such as harmony search algorithm (HS), particle swarm optimization algorithm (PSO), modified particle swarm optimization algorithm (MPSO), modified particle swarm optimization algorithm with time-varying acceleration coefficients (MPSO-TVAC), moth flame optimization (MFO), wolf optimization algorithm (WOA) and chimp optimization algorithm (ChOA). The experimental results show that using metaheuristic algorithms to fortify neural networks may increase their ability in finding optimal solutions. We also compare the results of our proposed algorithms with the results of the autoregressive integrated moving average (ARIMA) model. To compare the performance of the proposed algorithms and select the best one, we introduce eight estimation criteria for error assessment. Moreover, market efficiency is another important factor that is checked in this paper to avoid abnormal returns. Briefly speaking, it is the first time that ChOA and MFO algorithms have been used for the prediction of stock prices and to improve ANN. In addition, we use two algorithms (i.e., GA and GWO-PSO) for improving the feature selection process. Finally, experimental results show that WOA has the best performance among applied algorithms. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishi

关键词： Artificial neural network Chimp optimization algorithm Feature selection meta-heuristic algorithms Moth flame optimization algorithm Technical indicators

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Significance Relations for the Benchmarking of meta-heuristic algorithms 13

Significance Relations for the Benchmarking of Meta-Heuristi...

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13th International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Koeppen, Mario Ohnishi, Kei Kyushu Inst Technol Comp Sci & Engn Dept Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9781479935161

The experimental analysis of meta-heuristic algorithm performance is usually based on comparing average performance metric values over a set of algorithm instances. When algorithms getting tight in performance gains, the additional consideration of significance of a metric improvement comes into play. However, from this moment the comparison changes from an absolute to a relative mode. Here the implications of this paradigm shift are investigated. Significance relations are formally established. Based on this, a trade-ofT between increasing cycle-freeness of the relation and small maximum sets can be identified, allowing for the selection of a proper significance level and resulting ranking of a set of algorithms. The procedure is exemplified on the CEC'05 benchmark of real parameter single objective optimization problems. The significance relation here is based on awarding ranking points for relative performance gains, similar to the Borda count voting method or the Wilcoxon signed rank test. In the particular CEC'05 case, five ranks for algorithm performance can be clearly identified.

关键词： benchmarking meta-heuristic algorithms Borda count relational optimization

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Eddy Current Characterization Using Robust meta-heuristic algorithms for LS-SVM Hyper-Parameters Optimization 17

Eddy Current Characterization Using Robust Meta-Heuristic Al...

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17th IEEE Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Hacib, T. Le Bihan, Y. Chelabi, M. Boughedda, H. Mekideche, M. R. Univ Jijel Fac Sci & Technol Lab L2EI Jijel Algeria Univ Paris Sud UPMC CNRS Lab GeePsCent SupelecUMR 8507 Paris France

ISBN: (纸本)9781509010325

This paper presents the use of the Least Square Support Vector Machines (LS-SVM) technique, combined with the Finite Element Method (FEM), to characterize small cracks in order to get a fast non-destructive inspection. The LS-SVM is a statistical learning method that has good generalization capability and learning performance. LS-SVM trained model is proposed to predict crack sizing using experimental signals acquired from an Eddy Current (EC) sensor. The FEM is used to create the data set required to train this model. The performance of LS-SVM model depends on a careful setting of its associated hyper-parameters. Different tuning techniques for optimizing the LS-SVM hyper-parameters are studied: Electromagnetism-Like Mechanism (EM), Opposition Based Electromagnetism-Like Mechanism (OBEM) and Teaching Learning Based Optimization (TLBO). Results show that TLBO algorithm provides a good compromise between accuracy and computational cost.

关键词： EC characterization FEM LS-SVM Hyper-parameters optimization meta-heuristic algorithms

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Wireless Power Transfer Optimization Using meta-heuristic algorithms

Wireless Power Transfer Optimization Using Meta-heuristic Al...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Bennia, Fatima Boudouda, Aimad Nafa, Fares Univ Boumerdes Dept Elect Syst Engn Boumerdes Algeria Univ MHamed Bougara Boumerdes LIST Lab Boumerdes 35000 Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349740;9798350349757

The importance of Wireless Power Transfer (WPT) technology in biomedical implants to mitigate the risk of regeneration has increased significantly in recent years. WPT systems are dependent on key parameters such as the coupling coefficient (K), quality factor (Q), and mutual inductance (M), which play a crucial role in determining power transfer efficiency. These parameters are closely related to the geometric characteristics of the coils involved. Therefore, this study explores various meta-heuristic algorithms to search for optimal parameters that maximize power transfer efficiency. The initial results demonstrate that these algorithms perform well across different iterations. To confirm these findings, the study conducted comprehensive validation using Ansys Maxwell software to verify the optimal values obtained through optimization.

关键词： Biomedical implants Coupling Coefficient Efficiency Optimization meta-heuristic algorithms Mutual inductance Quality Factor Wireless Power Transfer

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Interest and Applicability of meta-heuristic algorithms in the Electrical Parameter Identification of Multiphase Machines

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ENERGIES 2019年第2期12卷 314页

作者： Gutierrez-Reina, Daniel Barrero, Federico Riveros, Jose Gonzalez-Prieto, Ignacio Toral, Sergio L. Duran, Mario J. Loyola Univ Andalusia Dept Engn Seville 41014 Spain Univ Seville Dept Elect Engn Seville 41092 Spain Univ Talca Fac Engn Curico 3340000 Chile Univ Huelva Thermal & Elect Engn Dept Huelva 21007 Spain Univ Malaga Dept Elect Engn E-29071 Malaga Spain

Multiphase machines are complex multi-variable electro-mechanical systems that are receiving special attention from industry due to their better fault tolerance and power-per-phase splitting characteristics compared with conventional three-phase machines. Their utility and interest are restricted to the definition of high-performance controllers, which strongly depends on the knowledge of the electrical parameters used in the multiphase machine model. This work presents the proof-of-concept of a new method based on particle swarm optimization and standstill time-domain tests. This proposed method is tested to estimate the electrical parameters of a five-phase induction machine. A reduction of the estimation error higher than 2.5% is obtained compared with gradient-based approaches.

关键词： multiphase drives off-line identification methods meta-heuristic algorithms

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Comparison of Three Chaotic meta-heuristic algorithms for the Optimal Design of Truss Structures with Frequency Constraints

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2023年第4期67卷 1130-1151页

作者： Kaveh, Ali Yosefpour, Hossein Iran Univ Sci & Technol Dept Civil Engn Tehran *** Iran Islamic Azad Univ Dept Civil Engn Maragheh Branch Maragheh *** Iran

The main reasons for the success of using chaos maps in meta-heuristic algorithms are fast optimization of non-linear and nonconvex problems. One of these cases is the control of the natural frequencies of structures to prevent the destructive and dangerous phenomenon of resonance. Natural frequencies have useful information about the dynamic behavior of structures, and by applying dynamic constraints, a significant improvement is achieved in the optimal design of structural weight. Applying frequency limits with traditional and gradient-based methods is very difficult and time-consuming, and in most cases, the calculation process stops at local optima. Recent research shows that chaos maps play a major role in escaping from local optima and reaching global optima. By combining these maps with meta- heuristic algorithms, while avoiding premature convergence, the access to global optima is accelerated and improved, and the ideal state of balance between the exploration and exploitation stages is realized. Today, chaotic algorithms are widely accepted by researchers and are considered as a challenging topic. In a recent research, six chaotic metaheuristic algorithms have been investigated for the formation and improvement of results with the optimal design of truss structures. In this part the chaotic algorithms include Chaotic Water Evaporation Optimization (CWEO), Chaotic Tug-of-War Optimization (CTWO) and Chaotic Thermal Exchange Optimization (CTEO) are examined.

关键词： meta-heuristic algorithms chaos maps exploration exploitation premature convergence

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Predicting daily heating energy consumption in residential buildings through integration of random forest model and meta-heuristic algorithms

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ENERGY 2024年 301卷

作者： Xu, Weiyan Tu, Jielei Xu, Ning Liu, Zuming Yunnan Normal Univ Coll Energy & Environm Sci Kunming 650000 Yunnan Peoples R China Yunnan Prov Rural Energy Engn Key Lab Kunming 650000 Yunnan Peoples R China Honghe Power Supply Bur Yunnan Power Grid Co Mengzi 661100 Peoples R China Universal Architecture Lab Shenzhen 518100 Guangdong Peoples R China

This research utilizes a sophisticated hybrid model integrating the Random Forest algorithm with meta -heuristic optimization techniques to estimate heating energy consumption in residential buildings. The study addresses key variables including architectural characteristics, occupancy, and ambient temperature. The primary objective is to enhance the prediction accuracy of heating energy consumption using a novel approach combining Random Forest with various meta -heuristic algorithms. The study employs six combinations of the Random Forest algorithm and meta -heuristic optimizers. To mitigate overfitting, K -Fold cross -validation is implemented during model training. The model ' s performance is evaluated using five statistical indices: coefficient of determination (R 2 ), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Theil Inequality Coefficient (TIC). Results demonstrate the hybrid model ' s high predictive accuracy, with the Arithmetic Optimization Algorithm enhancing Random Forest ' s performance significantly. Notable statistical achievements include R 2 = 0.977201, RMSE = 0.1179, MAE = 0.0573, RAE = 0.0930, and TIC = 0.0187. Additionally, the Ant Lion Optimizer shows excellent convergence, achieving a TIC value of 0.014986 after 101 iterations. The proposed hybrid model significantly outperforms traditional methods in predicting residential heating energy consumption. The integration of Random Forest with advanced meta -heuristic algorithms offers a robust framework for enhancing prediction accuracy in energy consumption modeling.

关键词： Energy consumption Residential buildings Heating load prediction Random forest meta-heuristic algorithms

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Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction

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JOURNAL OF CLEANER PRODUCTION 2022年 355卷

作者： Tran, Van Quan Univ Transport Technol 54 Trieu Khuc St Hanoi Vietnam

In the investigation, hybrid Gradient Boosting (GB) with fou r optimization techniques named Simulated Annealing (SA), Random Restart Hi l l Climbing (RRHC), Particle Swarm Optimization (PSO) and Evolution Strategy Optimization (ESO) are used to develop machine learning models for predicting unconfined compressive strength (UCS) of stabilized soils based on initial soi l properties , mix design and effective compaction. The results show that the hybrid model GB_PSO can successfully predict the UCS of stabilized soils with high performance R-2 = 0.9655, RMSE = 0.1633 MPa for testing part. The featu r e importance analyses are performed with using GB _PSO based Permutation Importance of Sklear n libra r y and Shapley Additive Explanations (SHAP value), which reveal that maximum dr y density (MDD), linear shrinkage (LS), and Gravel content are the most important characteristics in the UCS prediction performance of the GB_PSO model. The partial dependence plot (PDP) analysis consisting of PDP 1D and PDP 2D show respectively the impact w a y of each feature on the UCS and the interactions between initial soi l properties, mix design and effective compaction in the UCS improvement of stabilized soi l . The PDP analysis show that (i) the LS is the most important criterion in determining the success of improving the UCS of stabilized soil, (ii) the predominance of effective compaction over other conditions on the improvement of UCS. The present investigation can be considered to be new insights for better understanding process of stabilized soi l and UCS improvement.

关键词： Unconfined compressive strength Stabilized soil Gradient boosting meta-heuristic algorithms Machine learning Partial dependence plot

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MEALPY: An open-source library for latest meta-heuristic algorithms in Python

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JOURNAL OF SYSTEMS ARCHITECTURE 2023年第1期139卷

作者： Thieu, Nguyen Van Mirjalili, Seyedali PHENIKAA Univ Fac Comp Sci Yen NghiaHa Dong Hanoi 12116 Vietnam Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Obuda Univ Univ Res & Innovat Ctr Budapest Hungary Phenikaa Univ Hanoi Vietnam

meta-heuristic algorithms are becoming more prevalent and have been widely applied in various fields. There are numerous reasons for the success of such techniques in both science and industry, including but not limited to simplicity in search/optimization mechanisms, implementation readiness, black-box nature, and ease of use. Although the solutions obtained by such algorithms are not guaranteed to be exactly global optimal, they usually find reasonably good solutions in a reasonable time. Many algorithms have been proposed and developed in the last two decades. However, there is no library implementing meta-heuristic algorithms, which is easy to use and has a vast collection of algorithms. This paper proposes an open-source and cross-platform Python library for nature-inspired optimization algorithms called Mealpy. To propose Mealpy, we analyze the features of existing libraries for meta-heuristic algorithms. After, we propose the designation and the structure of Mealpy and validate it with a case study discussion. Compared with other libraries, our proposed Mealpy has the largest number of classical and state-of-the-art meta-heuristic algorithms, with more than 160 algorithms. Mealpy is an open-source library with well-documented code, has a simple interface, and benefits from minimum dependencies. Mealpy includes a wide range of well-known and recent meta-heuristics algorithms capable of optimizing challenge benchmark functions (e.g. CEC-2017). Mealpy can also be used for practical problems such as optimizing parameters for machine learning models. We invite the research community for widespread evaluations of this comprehensive library as a promising tool for research study and real-world optimization. The source codes, supplementary materials, and guidance is publicly available on GitHub: https://***/thieu1995/mealpy.

关键词： meta-heuristic algorithms Nature-inspired algorithms Swarm-based computing Global search optimization Optimization library Python software

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