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Gannet optimization algorithm enabled framework for spectrum sensing in OFDM based CR network

WIRELESS NETWORKS 2023年第6期29卷 2863-2872页

作者： Rao, D. Raghunatha Prasad, T. Jayachandra Prasad, M. N. Giri Jawaharlal Nehru Technol Univ Anantapur Anantapur Andhrapradesh India Jawaharlal Nehru Technol Univ Anantapur RGM Coll Engn & Technol Autonomous Anantapur Andhrapradesh India Jawaharlal Nehru Technol Univ JNTUA Coll Engn Ananthapuramu ECE Dept Constituent Coll Anantapur Andhrapradesh India

The emergence of the fifth generation (5G) mobile communication network highly promoted the enhancement of broadband wireless communication. One of the famous physical transmission technologies in regard to wireless communication is orthogonal frequency division multiplexing (OFDM) and the requirements of cognitive radio (CR) met by this OFDM. Spectrum sensing (SS) is a key enabling function in CR to improve utilization spectrum and eases the spectrum resources. Among more other modulation methods, OFDM is broadly utilized in various next generation and current wireless communications systems. This paper enables SS in OFDM based CR using proposed gannet optimization algorithm (GOA). Firstly, simulation is undergone and signal is received from OFDM based CR network. Here, generation of test statistics, such as signal energy, Eigen statistics, matched filter and wavelets done to ensure efficient CR communication without interference. Furthermore, fusion center undergoes fusion process, at which weights determined by proposed GOA and decision is finally processed. GOA diving patterns used for exploring the optimal region with in the search space and then enable exploitation phase to ensure better solution to compute weight vector. This research is evaluated by various performance metrics, such as mean square error and also bit error rate with values of 0.007 and 0.023, correspondingly for SNR of 20 dB with Nakagami fading channel.

关键词： Orthogonal frequency division multiplexing metaheuristic optimization algorithms Gannet optimization algorithm Cognitive radio Spectrum sensing Cooperative spectrum sensing Mean square error Bit error rate

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Integrated and optimized SVR analysis: Assessment of the preliminary and entire fracture energy of concrete

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10781-10798页

作者： Wang, Zhiyong Chongqing Technol & Business Inst Sch Urban Construct Engn Chongqing Peoples R China

Assessment of energy needed for crack growth in concrete structures has been an interesting topic since the use of fracture mechanics to concrete. However, experimental procedures need time, cost and efforts. Based on historical data, regression approaches were created using mechanical characteristics and mixed design factors to quantify the concrete preliminary (G(f)) and whole (G(F)) fracture energy. This work combined support vector regression (SVR) analysis with antlion optimization (ALO) and Harris Hawks optimization (HHO) approaches to build a hybridized SVR evaluation to fully comprehend Gf and G(F). Evaluation metrics demonstrate that both optimized ALO-SVR and HHO-SVR assessments could perform wonderfully throughout the estimation mechanism. Whenever the superior SVR investigation was contrasted to the literature, it was observed that the uniquely developed ALO-SVR regression also provides a reasonable boost in effectiveness, with benefits across the board. Finally, although the HHO-SVR technique has its particular capabilities in the simulating procedure, the ALO-SVR analysis seems to be highly reliable for determining G(f) and G(F).

关键词： Preliminary and entire fracture energy concrete SVR analysis metaheuristic optimization algorithms

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Short-term photovoltaic power production forecasting based on novel hybrid data-driven models

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JOURNAL OF BIG DATA 2023年第1期10卷 26页

作者： Alrashidi, Musaed Rahman, Saifur Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia Virginia Tech Adv Res Inst Bradley Dept Elect & Comp Engn Arlington VA 22203 USA

The uncertainty associated with photovoltaic (PV) systems is one of the core obstacles that hinder their seamless integration into power systems. The fluctuation, which is influenced by the weather conditions, poses significant challenges to local energy management systems. Hence, the accuracy of PV power forecasting is very important, particularly in regions with high PV penetrations. This study addresses this issue by presenting a framework of novel forecasting methodologies based on hybrid data-driven models. The proposed forecasting models hybridize Support Vector Regression (SVR) and Artificial Neural Network (ANN) with different metaheuristic optimization algorithms, namely Social Spider optimization, Particle Swarm optimization, Cuckoo Search optimization, and Neural Network Algorithm. These optimization algorithms are utilized to improve the predictive efficacy of SVR and ANN, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes. In addition, the proposed methodology aims to reduce the burden of random or manual estimation of such paraments and improve the robustness of the models that are subject to under and overfitting without proper tuning. The results of this study exhibit the superiority of the proposed models. The proposed SVR models show improvements compared to the default SVR models, with Root Mean Square Error between 12.001 and 50.079%. Therefore, the outcomes of this research work can uphold and support the ongoing efforts in developing accurate data-driven models for PV forecasting.

关键词： PV power forecast Machine learning metaheuristic optimization algorithms Hyperparameters and architectures tuning Feature selection

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An efficient equilibrium optimizer with support vector regression for stock market prediction

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 3165-3200页

作者： Houssein, Essam H. Dirar, Mahmoud Abualigah, Laith Mohamed, Waleed M. Minia Univ Fac Comp & Informat Al Minya 61519 Egypt Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Pulau Pinang Malaysia

A hybridized method that relies on using the support vector regression (SVR) method with equilibrium optimizer (EO) is proposed to foresee the closing prices of Egyptian Exchange (EGX). Three indices are modeled and employed: EGX 30, EGX 30 capped, and EGX 50 EWI. The efficiency of using the technical indicators and statistical measures in the forecasting process is evaluated. The proposed EO-SVR-based forecasting model is adopted and evaluated using mean absolute percentage error, average, standard deviation, best fit, worst fit, and CPU time. Also, it is compared with recently developed metaheuristic optimization algorithms published in the literature such as whale optimization algorithm, salp swarm algorithm, Harris Hawks optimization, gray wolf optimizer, Henry gas solubility optimization, Barnacles mating optimizer, Manta ray foraging optimization, and slime mold algorithm. The proposed EO-SVR model got better results than other the counterparts, and EO-SVR is considered the optimal model according to its superior outcomes. Moreover, there is no need to use technical indicators and statistical measures as their effect is not noticeable.

关键词： Equilibrium optimizer (EO) Support vector regression (SVR) Stock price prediction metaheuristic optimization algorithms Technical indicators

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Wrapper and Hybrid Feature Selection Methods Using metaheuristic algorithms for English Text Classification: A Systematic Review

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IEEE ACCESS 2022年 10卷 39833-39852页

作者： Alyasiri, Osamah Mohammed Cheah, Yu-N Abasi, Ammar Kamal Al-Janabi, Omar Mustafa Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Al Furat Al Awsat Tech Univ Karbala Tech Inst Karbala 56001 Iraq Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematically studied by exploring available studies of different metaheuristic algorithms used for FS to improve TC. This paper will contribute to the body of existing knowledge by answering four research questions (RQs): 1) What are the different approaches of FS that apply metaheuristic algorithms to improve TC? 2) Does applying metaheuristic algorithms for TC lead to better accuracy than the typical FS methods? 3) How effective are the modified, hybridized metaheuristic algorithms for text FS problems?, and 4) What are the gaps in the current studies and their future directions? These RQs led to a study of recent works on metaheuristic-based FS methods, their contributions, and limitations. Hence, a final list of thirty-seven (37) related articles was extracted and investigated to align with our RQs to generate new knowledge in the domain of study. Most of the conducted papers focused on addressing the TC in tandem with metaheuristic algorithms based on the wrapper and hybrid FS approaches. Future research should focus on using a hybrid-based FS approach as it intuitively handles complex optimization problems and potentiality provide new research opportunities in this rapidly developing field.

关键词： metaheuristics Feature extraction Text categorization Classification algorithms Systematics Search problems Business Feature selection text classification metaheuristic optimization algorithms wrapper-based approach hybrid-based approach

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Performance enhancement of a tuned mass damper for vibration suppression of an offshore jacket platform utilizing a novel metaheuristic optimization technique

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OCEAN ENGINEERING 2022年 263卷

作者： Shehata, Mohamed Abdelnaeem, Mohamed Mokhiamar, Ossama Alexandria Univ Fac Engn Mech Engn Dept Alexandria 21544 Egypt

Offshore platforms are key structures in energy provision that are subjected to continuous and harsh environmental loadings. A tuned mass damper (TMD) has been proposed as a robust and effective vibration control method for marine structures. However, selecting optimum TMD parameters under certain constraints may become a multimodal optimization problem. Several studies focused on finding the TMD optimum parameters using different metaheuristic optimization algorithms. Nonetheless, it becomes a necessity to find an answer to the question: which algorithm can find the most effective TMD parameters to minimize the structure response? This paper presents the use of a novel metaheuristic optimizer, the Improved Grey Wolf Optimizer (IGWO), for optimum tuning of a TMD aimed at reducing the wave-induced vibration level of a jacket type platform. Moreover, a comprehensive performance analysis of four different metaheuristic algorithms for optimizing a TMD has been investigated. They are IGWO, popular algorithms such as Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Whale optimization Algorithm (WOA). In order to achieve this purpose, a finite element (FE) model representing a prototype of a jacket-type platform under the influence of irregular waves is numerically solved in COMSOL Multiphysics. Irregular waves are presented using the JONSWAP spectrum and hydrodynamic forces are modeled using Morison's equation. The comparative algorithms are evaluated for the independent minimization of three different objective functions. The target parameters of TMD to be tuned are the mass ratio (mu), the frequency ratio (f(opt)), and the damping ratio (zeta(opt)). Finally, the optimized TMD will be applied to the FE model and the resultant response will be investigated. The results demonstrate the superior ability of the IGWO optimizer to optimally tune a TMD attached to marine structures compared with the other algorithms. In addition, IGWO proved its unique ability to

关键词： Offshore jacket platform Wave-induced vibration Dynamic time history analysis Tuned mass damper metaheuristic optimization algorithms Improved grey wolf optimizer

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HSSAHHO: a novel hybrid Salp Swarm-Harris Hawks optimization algorithm for complex engineering problems

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第9期14卷 11569-11605页

作者： Singh, Narinder Houssein, Essam H. Singh, S. B. Dhiman, Gaurav Punjabi Univ Dept Math Patiala Punjab India Minia Univ Fac Comp & Informat El Minya Egypt Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India

With metaheuristic algorithms hybridization is used typically to improve the performances of original algorithm, so it became a recent trend of research is to hybridize two and several number of variants to find out better quality of solution of practical and to tackle with recent real applications in the field of global optimization problems. Harris Hawks optimization (HHO) is powerful optimization tool that is robust, has smooth transitions between exploration and exploitation, that permits to provide competitive results in complex problems. Also, the Salp Swarm Algorithm (SSA) has been widely used to solve complex global optimization tasks due to its implementation simplicity and inexpensive computational overhead. However, SSA has premature convergence, is easily trapped in the local optimum solution and is ineffective in balancing exploration and exploitation. In this study, to overcome the shortcomings of HHO and SSA, a hybrid Salp Swarm Algorithm with Harris Hawks optimization (HHO) called (HSSAHHO) can be considered as a good choice for this purpose to solve these issues. Based on this consideration, hybrid SSAHHO is combination of SSA used for exploitation phase and HHO for exploration phase in uncertain environment. The movement directions and speed of the Salp' is improved using position update equations of HHO. To evaluate the effectiveness in solving the global optimization problems, HSSAHHO has been tested on 29-standard IEEE CEC 2017, 10-standard IEEE CEC 2020 benchmark test problems and also tested on six engineering design optimization problems. The numerical and statistical solutions obtained with HSSAHHO approach is compared with other meta-heuristics approaches such as Salp Swarm Optimizer (SSA), Particle Swarm optimization (PSO), Moth-flame optimization (MFO), Sine Cosine Algorithm (SCA), Enhanced grey wolf optimisation (EGWO), Augmented Grey Wolf Optimizer (AGWO) Hybrid Particle Swarm optimization-Grey Wolf Optimizer (PSOGWO), Harris Hawks Opti

关键词： Harris Hawks optimization metaheuristic optimization algorithms optimization Problems Salp Swarm Algorithm Swarm intelligence IEEE CEC-2017 and CEC-2020 benchmark functions

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Flood susceptibility modeling in the urban watershed of Guwahati using improved metaheuristic-based ensemble machine learning algorithms

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GEOCARTO INTERNATIONAL 2022年第26期37卷 12238-12266页

作者： Ahmed, Ishita Afreen Talukdar, Swapan Shahfahad Parvez, Ayesha Rihan, Mohd Baig, Mirza Razi Imam Rahman, Atiqur Jamia Millia Islamia Dept Geog Fac Nat Sci New Delhi India Univ Calif Irvine Henry Samueli Sch Engn Dept Elect Engn & Comp Sci Irvine CA USA

The urban watershed of Guwahati is a highly flood-prone region and the fastest growing city situated on the bank of the Brahmaputra River. Therefore, this study aims to the urban flood susceptibility mapping of Guwahati city using metaheuristic optimization algorithms integrated with random forest (RF) machine learning algorithm. Further, the receiver operating characteristic (ROC) and multiple error measurements were applied to analyze the performances of the models used. The result showed that about one-third of the area of Guwahati city is under the high and very high flood risk while nearly 50% area comes under low and very low flood risk. The value of the area under curve (AUC) of ROC was above 0.80 for all the integrated models applied. However, the RF-bee colony (BCO) and the RF-based ant colony (ACO) are the two best flood susceptibility models that performed better in the analysis. The methodology adopted in the study is cost and time effective and can be used for the flood susceptibility modeling in other parts of the world. Further, the findings of this study can useful in the flood mitigation and planning process.

关键词： Flood susceptibility mapping urban watershed metaheuristic optimization algorithms particle swarm optimization machine learning algorithms

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An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design optimization

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9610页

作者： Furio, Chiara Lamberti, Luciano Pruncu, Catalin I. Polytech Univ Bari Dept Mech Math & Management Via Edoardo Orabona4 I-70125 Bari Italy Buckinghamshire New Univ Sch Engn & Built Environm 59 Walton St Aylesbury HP21 7OG England

metaheuristic algorithms (MHAs) are widely used in engineering applications in view of their global optimization capability. Researchers continuously develop new MHAs trying to improve the computational efficiency of optimization search. However, most of the newly proposed algorithms rapidly lost their attractiveness right after their release. In the present study, two classical and powerful MHAs, namely the grey wolf optimizer (GWO) and the JAYA algorithm, which still attract the attention of optimization experts, were combined into a new hybrid algorithm called FHGWJA (Fast Hybrid Grey Wolf JAYA). FHGWJA utilized elitist strategies and repairing schemes to generate high-quality new trial solutions that may always improve the current best record or at least the old population. The proposed FHGWJA algorithm was successfully tested in seven engineering optimization problems formulated in the fields of robotics, hydraulics, and mechanical and civil engineering. Design examples included up to 29 optimization variables and 1200 nonlinear constraints. The optimization results proved that FHGWJA always was superior or very competitive with the other state-of-the-art MHAs including other GWO and JAYA variants. In fact, FHGWJA always converged to the global optimum and very often achieved 0 or nearly 0 standard deviation, with all optimization runs practically converging to the target design. Furthermore, FHGWJA always ranked 1st or 2nd in terms of average computational speed, and its fastest optimization runs were better or highly competitive with those of the best MHA taken for comparison.

关键词： metaheuristic optimization algorithms fast hybrid optimization algorithms GWO JAYA elitist strategies engineering problems

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A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms

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ENERGY 2024年 302卷

作者： Bian, Jianxiao Wang, Jiarui Yece, Qian Longdong Univ Sch Intelligent Mfg Qingyang Peoples R China Nanjing Normal Univ Sch Elect & Automat Engn Nanjing Peoples R China Chizhou Univ Coll Mech & Elect Engn Chizhou 247000 Peoples R China

Efficient power management is fundamental for organizations and systems to conserve resources, reduce costs, and improve environmental sustainability. To achieve the mentioned purposes, it is vital to perceive power consumption patterns and trends to make informed decisions about energy usage. By evaluating power consumption, inefficiencies and areas for improvement can be identified. In this regard, this paper presents a comprehensive study to predict power usage in a non-residential building HVAC system. The study employs the integration of machine learning models and optimization algorithms to achieve accurate predictions. The results demonstrate the effectiveness of the approach, with the CatBoost-AO model depicting superior performance in comparison with other models across a range of statistical evaluation metrics including NSE, JSD, RMSE, MAPE, VAF, KLD, as well as R 2 and runtime. Remarkably, the CatBoost-AO model achieves the maximum R 2 value of 0.91, indicating strong predictive capability. Overall, the presented study highlights the potential of employing machine learning and optimization techniques to improve power management and resource efficiency in HVAC systems, contributing to more sustainable and cost-effective operations in non-residential buildings.

关键词： Power consumption prediction HVAC systems Machine learning CatBoost model metaheuristic optimization algorithms

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