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A hybrid reptile search algorithm and Levenberg-Marquardt algorithm based Haar wavelets to solve regular and singular boundary value problems

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OPsearch 2023年第4期60卷 1793-1823页

作者： Omar, Hala A. Snasel, Vaclav Rizk-Allah, Rizk M. Menoufia Univ Fac Engn Basic Engn Sci Dept Shibin Al Kawm 32511 Egypt VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic

Various engineering applications lead to the appearance of partial differential equations resulting in boundary value problems (BVPs). Orthogonal collocation method based Haar wavelets has gained significant attention in solving these problems. The Haar wavelets have several properties like vanishing moment, compact effect, and orthogonality. These properties prioritize being used as base functions for solving BVPs. However, the approximation leads to a relatively high number of unknown coefficients, which needs an efficient and reliable nonlinear solver to reach their values. The Levenberg-Marquardt algorithm (LM) is one of the most efficient nonlinear solvers. However, it may diverge in case of too far initial guesses, especially in many unknowns. The reptile search algorithm (RSA) is a recent reliable, nature-inspired optimization technique that has a noticeable capability in dealing with high-dimensional issues. Therefore, this paper proposes a hybrid optimization algorithm that integrates the RSA and LM algorithms using Haar wavelets as bases, named RSA-LM-Haar algorithm, to solve regular and singular natures of the BVPs more efficiently. To evaluate the performance of the proposed RSA-LM-Haar algorithm, it is tested on twelve case studies of BVPs including the singular and regular problems. The results are compared with those based on the LM alone, named LM-Haar algorithm. Finally, the applicability of the proposed algorithm is verified using two practical chemical applications to prove its ability to solve real-time applications effectively. All the results affirmed the capability of the proposed algorithm in solving both regular and singular BVPs. All results and comparisons illustrated that the proposed hybridization algorithm provides remarkable performance.

关键词： Singular boundary value problem Differential equations reptile search algorithm Levenberg-Marquardt algorithm Hybridization

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A reptile search algorithm-based optimal control method for the hybrid pressure retarded osmosis and photovoltaic system 36

A reptile search algorithm-based optimal control method for ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Chen, Yingxue Chen, Huatao Gou, Linfeng Feng, Guanxiang Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Optimal control of photovoltaic systems (PV) and pressure retarded osmosis (PRO) systems has been a hot topic of research. The method used for maximum power tracking control has always deviated somewhat from the ideal and failed to achieve the expected results. The accuracy and speed of tracking in practical applications have always been a problem. Therefore, a metaheuristic-based reptile search algorithm (RSA) is proposed to solve the MPPT control problem of this system. Solar irradiance, ambient temperature and operating conditions parameters are used as input parameters of the PV/PRO system, and power is used as the output. Finally, the results of RSA were compared with the incremental conductance (INC), perturbation and observation (PO) and grey wolf (GWO) algorithms.

关键词： Photovoltaic Pressure retarded osmosis reptile search algorithm Maximum power point tracking

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Multivariate Bitcoin Price Prediction Based on Tuned Bidirectional Long Short-Term Memory Network and Enhanced reptile search algorithm 29th

Multivariate Bitcoin Price Prediction Based on Tuned Bidirec...

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29th International Conference on Information and Software Technologies (ICIST)

作者： Strumberger, Ivana Zivkovic, Miodrag Thumiki, Venkat Ram Raj Djordjevic, Aleksandar Gajic, Jelena Bacanin, Nebojsa Singidunum Univ Danijelova 32 Belgrade 11000 Serbia Modern Coll Business & Sci Muscat Oman

ISBN: (纸本)9783031489808;9783031489815

Cryptocurrency price prediction and investment is a popular and relevant area of business nowadays. It involves analyzing historical data to forecast future trends and movements in asset prices. Bitcoin has gained significant prominence in the worldwide financial market as an investment asset. However, the high volatility of its price has attracted considerable attention from researchers and investors alike, leading to a growing interest in understanding the factors that drive its movement. This paper builds upon a research and conducts an empirical approach into the time-series data of a diverse range of exogenous and endogenous variables. Specifically, in this paper, the closing prices of Bitcoin, Ethereum and the daily volume of Bitcoin-related tweets are examined. For forecasting closing Bitcoin price based on the above mentioned predictors, bidirectional long-short term memory (BiLSTM) network tuned by hybrid adaptive reptile search algorithm is proposed. The analysis covers a three-year period from January 2020 to August 2022 and employs a three-fold split of the data to train, validation, and testing datasets. The best generated model by algorithm introduced in this manuscript is compared to other BiLSTM networks tuned by other cutting-edge metaheuristics and achieved results revealed that the method introduced in this research outperformed all other competitors regarding standard regression metrics.

关键词： Cryptocurrency price forecasting Bitcoin, neural networks bidirectional long short-term memory metaheuristic optimization reptile search algorithm

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Using Manta Ray Foraging Mechanism to Improve reptile search algorithm 5

Using Manta Ray Foraging Mechanism to Improve Reptile Search...

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5th International Conference on Control and Robotics (ICCR)

作者： Zheng, Tao Zhang, Baohang Liu, Sicheng Todo, Yuki Gao, Shangce Univ Toyama Fac Engn Toyama Japan Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa Japan

ISBN: (纸本)9798350307627

The reptile search algorithm (RSA) is a novel, nature-inspired meta-heuristic algorithm. This algorithm mainly simulates the predatory behavior of crocodiles to achieve optimal solutions. It possesses rapid convergence speed along with robust optimization capabilities. However, due to its excessively fast convergence speed, RSA can easily fall into local optimality. This paper first proposes a hybrid algorithm called MRFORSA by adding the manta ray foraging optimization (MRFO) mechanism to help RSA better jump out of the local optimum. Experimental findings using the IEEE CEC2017 benchmark function indicate that MRFORSA exhibits strengths in terms of solution quality and convergence velocity.

关键词： Manta Ray Foraging Mechanism reptile search algorithm Hybrid algorithm

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Sine Cosine reptile search algorithm with Grid search Support Vector Machine based Ransomware Detection and Classification 2

Sine Cosine Reptile Search Algorithm with Grid Search Suppor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Adnan, Myasar Mundher Devi, S. Vaag Deepthi, V.S. Raghunathareddy, M.V. Yamsani, Nagendar The Islamic University Najaf Iraq City Engineering College Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India Cyber Security Dayananda Sagar College of Engineering Department of Computer Science and Engineering Karnataka Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350317558

The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine reptile search algorithm with Grid search Support Vector Machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and Artificial Neural Network (ANN). © 2024 IEEE.

关键词： +Support Vector Machine Grid search Min-max Normalization Ransomware reptile search algorithm

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A binary reptile search algorithm based on transfer functions with a new stochastic repair method for 0-1 knapsack problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年 178卷

作者： Ervural, Bilal Hakli, Huseyin Necmettin Erbakan Univ Dept Ind Engn TR-42090 Konya Turkiye Necmettin Erbakan Univ Dept Comp Engn TR-42090 Konya Turkiye

The reptile search algorithm (RSA), inspired by crocodiles' hunting behavior, is a recently introduced nature -inspired algorithm. Although the original version of the RSA shows outstanding performance in optimizing continuous applications, it is not suitable for discrete optimization problems like 0-1 knapsack problems (0-1 KP). To extend RSA to binary optimization issues, binary RSA (BinRSA) is proposed in this study. A wide range of transfer functions (TFs), including the largely used s-shaped and v-shaped, and recently introduced z-shaped, u -shaped, and taper-shaped, are investigated in the proposed algorithm to map the continuous values into binary. In addition, a novel repair method is introduced to cope with infeasible solutions for 0-1 KP and discussed in detail regarding its efficacy in reaching the optimal solution. The proposed method is validated on three benchmark datasets with 63 instances of 0-1 KP. First, the impact of 25 different transfer functions under six categories on the performance of the proposed binary algorithm is thoroughly investigated, and the results indicate that the taper-shaped T1 transfer function is superior to the other variants of the BinRSA. Then, the effectiveness of the proposed BinRSA with T1 transfer function is compared with some well-known and state-of -art algorithms, including Harris hawks optimization (HHO), slime mould algorithm (SMA), and marine predators algorithm (MPA). The experimental results show that compared to other methods, BinRSA considerably increased the solution accuracy and robustness for solving 0-1 KP.

关键词： reptile search algorithm Knapsack problems Binary optimization Repair method Transfer functions

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Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

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ENERGY SCIENCE & ENGINEERING 2023年第12期11卷 4635-4665页

作者： Hachemi, Ahmed T. Sadaoui, Fares Arif, Salem Saim, Abdelhakim Ebeed, Mohamed Kamel, Salah Jurado, Francisco Mohamed, Emad A. Univ Kasdi Merbah Ouargla Elect Engn Lab Ouargla Algeria Univ Amar Telidji LACoSERE Lab Laghouat Algeria Nantes Univ IREENA Lab St Nazaire France Sohag Univ Fac Engn Dept Elect Engn Sohag Egypt Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt Univ Jaen Dept Elect Engn Jaen Spain Prince Sattam Bin Abdulaziz Univ Dept Elect Engn Al Kharj Saudi Arabia

This paper introduces a Modified reptile search algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs-based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness-distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard reptile search algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112-bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case. The overall implementation diagram of the proposed Modified reptile search algorithm for the optimal operation of distribution ***

关键词： optimal distribution networks planning optimal operation renewable energy sources reptile search algorithm uncertainty

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MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

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ENERGY 2024年 293卷

作者： Zheng, Yue Wang, Jie-Sheng Zhu, Jun-Hua Zhang, Xin-Yue Xing, Yu-Xuan Zhang, Yun-Hao Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Liaoning Peoples R China

The proposal of the wind farm layout optimization (WFLO) problem aims to better utilize wind energy. A multiobjective reptile search algorithm (MORSA) based on elite non -dominated sorting and grid indexing mechanism was proposed to solve the multi -objective optimization problem of wind farm layout under the Jansen wake model to maximize power generation while minimizing costs. Firstly, the elite non -dominated sorting method was used to sort the population, and then the crowding distance method was used to maintain the diversity between the optimal sets. By calculating the crowding distance, the same level of non -dominated populations are sorted. Then, the grid index mechanism is added to the archive for selection and deletion. Leaders can use the roulette wheel to select and delete some solutions in high -density grids. By obtaining the optimal Pareto solution set while maintaining the diversity of the population, the effectiveness of solving multi -objective optimization problems is improved. The improved algorithm solves the WFLO problem based on the Jansen wake model by using two different initial self -flow winds for performance testing while considering the effects of single wake and multiple wake with or without partial wake occlusion. The performance of the WFLO (output power and cost) was compared without considering wake obstruction, MORSA can obtain the minimum turbine cost of 26.3601 and the maximum power generation of 2.3078E-08 with a single wake flow of 16 m/s. The experimental results show that the proposed MORSA has better convergence and applicability, and has shown good results in multi -objective layout optimization of wind farms.

关键词： Wind farm layout optimization reptile search algorithm Jensen wake model Wind turbines Multi-objective optimization Elite non dominated ranking Grid indexing mechanism

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A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 152卷 106404-106404页

作者： Emam, Marwa M. Houssein, Essam H. Ghoniem, Rania M. Minia Univ Fac Comp & Informat Al Minya Egypt Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Mansoura Univ Dept Comp Mansoura Egypt

In this paper, we proposed an enhanced reptile search algorithm (RSA) for global optimization and selected optimal thresholding values for multilevel image segmentation. RSA is a recent metaheuristic optimization algorithm depending on the hunting behavior of crocodiles. RSA is inclined to inadequate diversity, local optima, and unbalanced exploitation abilities as other metaheuristic algorithms. The RUNge Kutta optimizer (RUN) is a novel metaheuristic algorithm that has demonstrated effectiveness in solving real-world optimization problems. The enhanced solution quality (ESQ) in RUN utilizes the thus-far best solution to promote the quality of solutions, improve the convergence speed, and effectively balance the exploration and exploitation steps. Also, the Scale factor (SF) has a randomized adaptation nature, which helps RUN in further improving the exploration and exploitation steps. This parameter ensures a smooth transition from exploration to exploitation. In order to mitigate the drawbacks of the RSA algorithm, this paper proposed a modified RSA (mRSA), which combines the RSA algorithm with the RUN. The ESQ mechanism and the scale factor boost the original RSA's performance, enhance convergence speed, bypass local optimum, and enhance the balance between exploitation and exploration. The validity of mRSA was verified using two experimental sequences. First, we applied mRSA to CEC'2020 benchmark functions of various types and dimensions, showing that mRSA has more robust search capabilities than the original RSA and popular counterpart algorithms concerning statistical, convergence, and diversity measurements. The second experiment evaluated mRSA for a real-world application to solve magnetic resonance imaging (MRI) brain image segmentation. Overall experimental results confirm that the mRSA has a strong optimization ability. Also, mRSA method is a more successful multilevel thresholding segmentation and outperforms comparison methods according to different pe

关键词： reptile search algorithm RUNge kutta optimizer Metaheuristics Global optimization Image segmentation Multi-level thresholding Brain tumor images

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A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction

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ENERGY 2023年第1期266卷

作者： Xiong, Jinlin Peng, Tian Tao, Zihan Zhang, Chu Song, Shihao Nazir, Muhammad Shahzad Huaiyin Inst Technol Fac Automation Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Accurate wind power forecast is critical to the efficient and safe running of power systems. A hybrid model that combines complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), random forest (RF), improved reptile search algorithm (IRSA), bidirectional long short-term memory (BiLSTM) network and extreme learning machine (ELM) is proposed for wind power prediction in this paper. Firstly, the CEEMD decomposes the non-stationary original wind power sequence into comparatively stationary modal components, and sample entropy aggregation is used to decrease the computational complexity. Secondly, redundant features are further eliminated through random forest feature selection. Thirdly, the BiLSTM model and the ELM model are applied to forecast high and low frequency components, respectively. IRSA is used to optimize the model's parameters. Finally, the predicted value of each component is summed to arrive at the final predicted value of wind power. By comparing with ten other models, the results show that the dual-scale ensemble model of BiLSTM and ELM can obtain better prediction accuracy. The RMSE of the model proposed in this study is reduced by more than 10% compared with other benchmark models, which demonstrates that the proposed model can better fit the wind power data and achieve better prediction results.

关键词： Wind power prediction BiLSTM ELM CEEMD reptile search algorithm

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