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An Enhanced Imperialist Competitive Algorithm for optimum design of skeletal structures

SWARM AND EVOLUTIONARY COMPUTATION 2018年 40卷 24-36页

作者： Maheri, Mahmoud R. Talezadeh, M. Shiraz Univ Dept Civil Engn Shiraz Iran

An Enhanced Imperialist Competitive Algorithm (EICA) is proposed aimed at enabling the ICA algorithm to escape from local optima faster, thus enabling faster convergence of the basic ICA algorithm. To this end, the imperialistic competition phase of the algorithm is enhanced by giving added value to a slightly unfeasible solution, based on its distance from the relative imperialist. The performance of the proposed EICA algorithm is investigated through design optimizations of four benchmark side-sway frames. Results indicate that, in terms of both the design quality and the solution speed, EICA compares significantly favourable with a number of other meta heuristic optimizers, including the basic ICA.

关键词： Imperialistic competitive algorithm Design optimization metaheuristic optimization algorithms Steel frames

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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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Comparative Study of Supervised Learning and metaheuristic algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms

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IEEE ACCESS 2019年 7卷 26123-26135页

作者： Lovon-Melgarejo, Jesus Castillo-Cara, Manuel Huarcaya-Canal, Oscar Orozco-Barbosa, Luis Garcia-Varea, Ismael Univ Nacl Ingn Ctr Informat & Commun Technol Lima 25 Peru Univ Castilla La Mancha Res Inst Informat Albacete 02071 Spain

The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors;2) wireless networks and;3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.

关键词： Indoor positioning fingerprinting Bluetooth classification model signal processing received signal strength indication multipath fading transmission power benchmark metaheuristic optimization algorithms

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An enhanced harmony search algorithm for optimum design of side sway steel frames

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COMPUTERS & STRUCTURES 2014年 136卷 78-89页

作者： Maheri, Mahmoud R. Narimani, M. M. Shiraz Univ Dept Civil Engn Shiraz Iran

An enhanced harmony search (EMS) algorithm is developed enabling the HS algorithm to quickly escape from local optima. For this purpose, the harmony memory updating phase is enhanced by considering also designs that are worse than the worst design stored in the harmony memory but are far enough from local optima. The proposed EHS algorithm is utilized to solve four classical weight minimization problems of steel frames. Results indicate that, as far as the quality of optimum design and convergence behavior are concerned, EHS is significantly superior or definitely competitive with other meta-heuristic optimization algorithms including the classical HS. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： metaheuristic optimization algorithms Global search Enhanced harmony search Optimum design of steel frames

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A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第41期30卷 93407-93421页

作者： Revathi, B. Sri Vellore Inst Technol Sch Elect Engn Chennai 600127 Tamil Nadu India

The sustainability of the earth depends on renewable energy. Forecasting the output of renewable energy has a big impact on how we operate and manage our power networks. Accurate forecasting of renewable energy generation is crucial to ensuring grid dependability and permanence and reducing the risk and cost of the energy market and infrastructure. Although there are several approaches to forecasting solar radiation on a global scale, the two most common ones are machine learning algorithms and cloud pictures combined with physical models. The objective is to present a summary of machine learning-based techniques for solar irradiation forecasting in this context. Renewable energy is being used more and more in the world's energy grid. Numerous strategies, including hybrids, physical models, statistical approaches, and artificial intelligence techniques, have been developed to anticipate the use of renewable energy. This paper examines methods for forecasting renewable energy based on deep learning and machine learning. Review and analysis of deep learning and machine learning forecasts for renewable energy come first. The second paragraph describes metaheuristic optimization techniques for renewable energy. The third topic was the open issue of projecting renewable energy. I will wrap up with a few potential future job objectives.

关键词： Renewable energy Machine learning Deep learning metaheuristic optimization algorithms Neural network Power prediction

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Evaluation of students' performance during the academic period using the XG-Boost Classifier-Enhanced AEO hybrid model

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期238卷

作者： Cheng, Biqian Liu, Yuping Jia, Yunjian China Univ Polit Sci & Law Postdoctoral Stn Beijing 100088 Peoples R China China Univ Polit Sci & Law Sch Criminal Justice Beijing 100088 Peoples R China Chongqing Univ Coll Commun Engn Chongqing 400044 Peoples R China

The proactive prediction and systematic classification of students' academic performance empower educational administrators with the invaluable capability to not only pinpoint potential challenges but also to craft targeted strategies and interventions that are tailored to the unique needs of students. This multifaceted approach enables educational institutions to proactively address issues within the education system, fostering a more equitable and effective learning environment for all, while simultaneously fostering a culture of continuous improvement and accountability in the pursuit of educational excellence. Hence, the current investigation aims to classify and predict the students' performance by examining and comparing the machine learning and artificial neural network assessments. Five methods of the Random Forest Classifier, the Decision Tree Classifier, the K Neighbors Classifier, the MLP Classifier, and the XG-Boost Classifier are used. These methods' performances are compared through the accuracy, precision, recall, and F1-score indicators. This comparison is applied to the base data and balanced data, which is carried out by the SVM-SMOTE technique. Finally, five metaheuristic algorithms are applied to the selected method to evaluate the performance indicators of the hybrid models. The results indicate that applying the SVM-SMOTE technique improves the methods' performance, in which the XG-Boost represented the best performance. As a result, the metaheuristic algorithms are applied to the XG-Boost, yielding to 9.33%, 8.44%, 9.33%, and 9.27% enhancement of the Accuracy, Precision, Recall and F1-Score values. Subsequently, the Enhanced Artificial Ecosystem-Based optimization + XG-Boost hybrid method provides the accuracy and F1-score values of 0.9417 and 0.9413. These results underscore the potential of combining machine learning techniques with metaheuristic algorithms to enhance the accuracy and effectiveness of predicting and classifying students' per

关键词： Student performance evaluation Machine learning methods metaheuristic optimization algorithms XG-Boost Classifier

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Modified bald eagle search algorithm for lithium-ion battery model parameters extraction

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ISA TRANSACTIONS 2023年 134卷 357-379页

作者： Ferahtia, Seydali Rezk, Hegazy Djerioui, Ali Houari, Azeddine Motahhir, Saad Zeghlache, Samir Univ Msila Lab Genie Elect Dept Elect Engn Msila Algeria Prince Sattam bin Abdulaziz Univ Coll Engn Wadi Alddawasir Dept Elect Engn Al Kharj Saudi Arabia Minia Univ Fac Engn Al Minya Egypt Nantes Univ IREENA Lab Nantes France USMBA ENSA Fes Morocco Univ Msila Dept Elect Engn Lab Anal Signaux & Syst Msila Algeria

Bald eagle search algorithm (BES) is a recent metaheuristic algorithm based on bald eagle hunting behavior. Like other metaheuristic algorithms, the BES algorithm is prone to entangle in local optimums due to limited diversity, sluggish convergence rate, or improper equilibrium between exploitation and exploration. Thus, adaptive parameters are injected into the original BES to overcome these shortcomings. These parameters are a function of the current and the max number of iterations. They provide the eagle with more diversity during the exploration and exploitation phases. The modified BES is tested on test functions provided by Congress on Evolutionary Computation 2020 and Congress on Evolutionary Computation 2022. The obtained results are compared to that of other reliable and recent algorithms. In addition, analysis of variance and Tuckey tests are utilized to confirm the results' significance. Due to its benefits, lithium-ion batteries are employed in more and more applications. However, extracting its parameters is challenging due to its complex model. Hence, the proposed algorithm will handle this task to approve its performance in complex problems. The significant benefit of this extraction method is its excellent precision, with fitness value declining (root mean square error) to 0.89 x 10-3 compared to the original BES (1.013 x 10-3) with a standard deviation of 1.12 x 10-3. To confirm the performance of mBES, a second battery was tested with the New European Driving Cycle profile. The mBES has the lowest fitness values (0.058896) and the least standard deviation (5.89 x 10-7).(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Bald eagle search algorithm (BES) metaheuristic optimization algorithms (MAs) Lithium-ion battery model Parameters identification

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A Comprehensive Review on Stochastic Optimal Power Flow Problems and Solution Methodologies

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IETE TECHNICAL REVIEW 2024年第2期41卷 147-174页

作者： Maheshwari, Ankur Sood, Yog Raj Jaiswal, Supriya Natl Inst Technol Elect Engn Hamirpur Himachal Prades India Natl Inst Technol Dept Elect Engn Hamirpur Himachal Prades India

The deregulation of the electricity market has been accompanied by the growing utilization of unpredictable renewable energy sources (RESs) such as solar, wind, and hydropower plants. Additionally, advancements in energy storage technologies and new energy demands have further contributed to this trend. As a result, the planning and operation of power systems are now surrounded by a higher level of uncertainty. In order to ensure the proper operation of power systems integrated with RESs, modern power systems are equipped with specific vital tools such as optimal power flow (OPF), which regulates generation and demand to achieve specific objectives. Hence, this paper conducts a comprehensive review of recently published research articles focusing on various solution strategies to address OPF problems in the presence of stochastic RESs and power demand. The review encompasses diverse solution methodologies, objective functions, constraints, and distinct techniques to simulate the stochastic behavior of RESs and dynamic loads. Additionally, the paper explores fundamental challenges, identifies critical research gaps, and highlights unexplored areas pertaining to optimal power system operation in the future. This review is essential for system operators who need to assess and pre-plan flexibility competency for their power systems to ensure practical and cost-effective operation under high RESs penetration.

关键词： metaheuristic optimization algorithms Optimal power flow Probability density functions Renewable energy sources Renewable uncertainty

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

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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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