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hunger games search algorithm for global optimization of engineering design problems

MATERIALS TESTING 2022年第4期64卷 524-532页

作者： Mehta, Pranav Yildiz, Betul Sultan Sait, Sadiq M. Yildiz, Ali Riza Bursa Uludag Univ Dept Elect & Energy Bursa Turkey Dharmsinh Desai Univ Dept Mech Engn Nadiad Gujarat India King Fahd Univ Petr & Minerals Dhahran Saudi Arabia Bursa Uludag Univ Dept Mech Engn Bursa Turkey

The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-a-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.

关键词： hunger games search algorithm optimization optimum design suspension arm vehicle design

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Global Optimization of Self-Potential Anomalies Using hunger games search algorithm

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PURE AND APPLIED GEOPHYSICS 2024年第4期181卷 1303-1336页

作者： Ai, Hanbing Li, Hongxing Essa, Khalid S. Ekinci, Yunus Levent Balkaya, Caglayan China Univ Geosci Sch Geophys & Geomat Wuhan Hubei Peoples R China East China Univ Technol Sch Geophys & Measurement Control Technol Nanchang Jiangxi Peoples R China Cairo Univ Fac Sci Geophys Dept Giza Egypt Bitlis Eren Univ Dept Art Hist Bitlis Turkiye Suleyman Demirel Univ Dept Geophys Engn Isparta Turkiye

In this paper, the effectiveness of recently proposed hunger games search (HGS) optimization tool, which simulates behavioural choices of the predators in the animal kingdom, in estimating model parameters of idealized causative sources from Self-Potential (SP) anomalies is presented. Before the parameter estimations, the modal analyses of the model parameter pairs and parameter tuning processes were performed to characterize the optimization problem and to decide the optimal value of the control parameter of HGS algorithm, respectively. Subsequently, the proposed optimizer was applied on some synthetic SP data sets and four anomalies obtained from India, Turkiye, Canada, and Indonesia. Multiple and intercalating causative sources were considered in the synthetic and real data cases, and HGS algorithm produced satisfactory solutions. In the composite anomaly cases a second moving average procedure successfully removed the undesired background effects. Uncertainty appraisal analyses showed the validity of the model parameter estimations. The outputs of HGS optimizer were also compared with those of the well-established and popular standard particle swarm optimization regarding convergence rate, robustness, stability, and accuracy. Applications presented here clearly showed that SP anomalies can be inverted more effectively via HGS since the procedure has the ability to search the predefined model space extensively without being trapped into numerous local minima and approximate the global minimum more precisely. Thus, it is recommended to use the HGS algorithm in model parameter estimation studies using SP anomalies.

关键词： Self-potential anomalies parameter estimations hunger games search algorithm statistical analyses

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Parameter Identification of Proton Exchange Membrane Fuel Cell Based on hunger games search algorithm

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ENERGIES 2021年第16期14卷 5022-5022页

作者： Fahim, Samuel Raafat Hasanien, Hany M. Turky, Rania A. Alkuhayli, Abdulaziz Al-Shamma'a, Abdullrahman A. Noman, Abdullah M. Tostado-Veliz, Marcos Jurado, Francisco Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Elect Engn Dept Cairo 11835 Egypt King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Univ Jaen Super Polytech Sch Linares Dept Elect Engn Linares 23700 Spain

This paper presents a novel minimum seeking algorithm referred to as the hunger games search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses.

关键词： hunger games search algorithm hydrogen modeling and simulations parameter identification PEMFCs

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HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期37卷 52-104页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Chakraborty, Ratul Saha, Moumita Nama, Sukanta Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Math Agartala 799046 Tripura India Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India Directorate Informat Technol Agartala Tripura India Maharaja Bir Bikram Univ Dept Appl Math Agartala Tripura India

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm hunger games search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.

关键词： benchmark functions hunger games search algorithm IEEE CEC 2019 functions Nemenyi multiple comparison test whale optimization algorithm

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Road Damage Detection Using the hunger games search with Elman Neural Network on High-Resolution Remote Sensing Images

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REMOTE SENSING 2022年第24期14卷 6222-6222页

作者： Al Duhayyim, Mesfer Malibari, Areej A. Alharbi, Abdullah Afef, Kallekh Yafoz, Ayman Alsini, Raed Alghushairy, Omar Mohsen, Heba Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Aflaj Dept Comp Sci Al Kharj 11942 Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Engn Dept Ind & Syst Engn POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia King Khalid Univ Coll Sci & Arts Mahayil Dept Math Abha 62521 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia Univ Jeddah Coll Comp Sci & Engn Dept Informat Syst & Technol Jeddah 21589 Saudi Arabia Future Univ Egypt Fac Comp & Informat Technol Dept Comp Sci New Cairo 11835 Egypt

Roads can be significant traffic lifelines that can be damaged by collapsed tree branches, landslide rubble, and buildings debris. Thus, road damage detection and evaluation by utilizing High-Resolution Remote Sensing Images (RSI) are highly important to maintain routes in optimal conditions and execute rescue operations. Detecting damaged road areas through high-resolution aerial images could promote faster and effectual disaster management and decision making. Several techniques for the prediction and detection of road damage caused by earthquakes are available. Recently, computer vision (CV) techniques have appeared as an optimal solution for road damage automated inspection. This article presents a new Road Damage Detection modality using the hunger games search with Elman Neural Network (RDD-HGSENN) on High-Resolution RSIs. The presented RDD-HGSENN technique mainly aims to determine road damages using RSIs. In the presented RDD-HGSENN technique, the RetinaNet model was applied for damage detection on a road. In addition, the RDD-HGSENN technique can perform road damage classification using the ENN model. To tune the ENN parameters automatically, the HGS algorithm was exploited in this work. To examine the enhanced outcomes of the presented RDD-HGSENN technique, a comprehensive set of simulations were conducted. The experimental outcomes demonstrated the improved performance of the RDD-HGSENN technique with respect to recent approaches in relation to several measures.

关键词： hunger games search algorithm remote sensing road damage detection computer vision deep learning object detection

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Predicting compressive strength of fiber-reinforced coral aggregate concrete: Interpretable optimized XGBoost model and experimental validation

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STRUCTURES 2024年 64卷

作者： Sun, Zhen Wang, Xin Huang, Huang Yang, Yuxi Wu, Zhishen Southeast Univ Natl & Local Unified Engn Res Ctr Basalt Fiber Pro Key Lab C & PC Struct Minist Educ Nanjing 211189 Peoples R China Nanjing Res Inst Intelligent Infrastruct Nanjing 210012 Peoples R China Xiamen Hongye Co Ltd China Bldg Mat Test & Certificat Grp Xiamen 361100 Peoples R China

The addition of macro and micro fibers can enhance the compressive strength of fiber-reinforced coral aggregate concrete (FRCAC-CS). Traditional explicit models for FRCAC-CS suffer from shortcomings related to accuracy and generalization capabilities. To address this issue, a comprehensive database for FRCAC-CS was established by collecting 834 sets of experimental data from 15 literature sources. The hunger games search algorithm (HGS), sooty tern optimization algorithm, and whales optimization algorithm were used to optimize the hyperparameters of the XGBoost algorithm, resulting in optimized XGBoost models for FRCAC-CS. The performance of the prediction models was assessed through comparisons between predicted and actual values, model residual distribution, and performance evaluation metrics. Sensitivity analysis was conducted using the Shapley additive explanations method. Subsequently, 9 different mix proportions were formulated to validate the performance of the optimal model. The results suggest that the HGS-XGBoost model yields predictions that are closer to the actual values, exhibiting smaller mean and standard deviation in model residuals. The five major performance evaluation metrics, including RMSE, MSE, MAPE, MAE, and R2, for the HGS-XGBoost model are 0.91, 0.83, 2.35, 0.77, and 0.99, respectively, surpassing those of other models. Sensitivity analysis highlights the water-to-binder ratio and total aggregate content as the two most crucial factors among the mix components. Decreasing the water-to-binder ratio and the total aggregate content improves the FRCAC-CS. The experimental results from the 9 mix proportions demonstrate that the error between predicted and experimental values is below 7%, affirming the HGS-XGBoost model's adequate accuracy and generalization capabilities for new datasets. This study introduces a novel AI-based prediction method for FRCAC-CS, establishing the groundwork for its potential application in reef construction.

关键词： Compressive strength Fiber -reinforced coral aggregate concrete hunger games search algorithm Shapley additive explanations Machine learning

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A novel-enhanced metaheuristic algorithm for FOPID-controlled and Bode's ideal transfer function-based buck converter system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第10期45卷 1854-1872页

作者： Izci, Davut Ekinci, Serdar Batman Univ Dept Comp Engn Batman Turkiye Batman Univ Dept Comp Engn TR-72100 Batman Turkiye

The search for a better approach to design a convenient controlling scheme is one of the main challenges of the research field related to power converters. This paper brings forward a significant contribution as part of this challenge. In this regard, novel approaches have been proposed for both controller scheme and tuning algorithm. In terms of controlling scheme, a fractional-order proportional-integral-derivative (FOPID) controller was used alongside the Bode's ideal transfer function to achieve optimum performance of a buck converter system. To achieve a greater performance of the FOPID controller, a novel metaheuristic algorithm, which consists of a balanced structure in terms of explorative and exploitative phases, has also been developed by hybridizing the hunger games search algorithm with simulated annealing technique. The proposed hybrid algorithm helped tuning the FOPID controller optimally. The proposed algorithm and controlling scheme have been proved to be a better approach for controlling a buck converter system in terms of time and frequency domains along with disturbance rejection. To further verify the effectiveness of the proposed hybrid algorithm-based controlling scheme, it was compared with other reported and capable approaches which have also confirmed the greater performance of the proposed approach.

关键词： hunger games search algorithm simulated annealing technique metaheuristics fractional-order PID controller buck converter

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Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods

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WATER 2021年第23期13卷 3379-3379页

作者： Adnan, Rana Muhammad Mostafa, Reham R. Islam, Abu Reza Md. Towfiqul Gorgij, Alireza Docheshmeh Kuriqi, Alban Kisi, Ozgur Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Mansoura Univ Fac Comp & Informat Sci Informat Syst Dept Mansoura 35516 Egypt Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh Univ Sistan & Baluchestan Fac Ind & Min Khash Zahedan *** Iran Univ Lisbon Inst Super Tecn CERIS P-1049001 Lisbon Portugal Univ Appl Sci Dept Civil Engn D-23562 Lubeck Germany Ilia State Univ Dept Civil Engn Tbilisi 0162 Georgia

Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and the determination coefficient (R-2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting;the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling.

关键词： drought modeling standard precipitation index random vector functional link hunger games search algorithm

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A hybrid adaptive Differential Evolution based on Gaussian tail mutation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期119卷

作者： Chen, Hui Li, Shaolang Li, Xiaobo Zhao, Yuxin Dong, Junwei Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China

In this paper, we propose an improved version of JADE by hybridizing the JADE algorithm with a Gaussian tail, a modified hunger games search (HGS) algorithm, and a distance-based multi-population (DbMP) approach named as HJADE-GT. In the proposed algorithm, two sets of operators (modified HGS operator and JADE operator with Gaussian tail) are utilized to generate offspring to further enhance the exploration and exploitation abilities. DbMP approach is proposed to make full use of feedback information from the whole population. In HJADE-GT, the main population is divided into three fixed-size subpopulations: exploration subpopulation, balanced subpopulation, and exploitation subpopulation. Secondly, a modified HGS operator is incorporated into the exploration subpopulation to improve global searchability. Thirdly, the JADE operator with a Gaussian tail is utilized to enhance the ability of exploitation subpopulation. Finally, the DbMP approach is utilized for the balanced subpopulation to choose an appropriate operator for current individuals to make full use of feedback information from the exploration subpopulation and exploitation subpopulation. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance with 13 well-known algorithms, including jDE, SaDE, JADE, MPEDE, SHADE, CoDE, SaJADE, iLSHADE, jSO, CMAES, MGFPA, ESSA, and PPSO on CEC2017 benchmark functions. Four engineering problems and three dynamic economic emission dispatch (DEED) problems were utilized to verify the performance of HJADE-GT, and the experiments on DEED problems confirm that HJADE-GT is an efficient algorithm to solve engineering and large-scale constrained DEED problems.

关键词： Differential evolution hunger games search algorithm Distance-based multi-population Continuous optimization problem Engineering problem Dynamic economic emission dispatch

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Optimizing Supply Chain Efficiency Using Innovative Goal Programming and Advanced Metaheuristic Techniques

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APPLIED SCIENCES-BASEL 2024年第16期14卷 7151页

作者： Douaioui, Kaoutar Benmoussa, Othmane Ahlaqqach, Mustapha Hassan 1 Univ Fac Sci & Tech Lab Engn Ind Management & Innovat Settat 26000 Morocco Euromed Univ Fes Euromed Polytech Sch Fes 30030 Morocco Hassan II Univ Casablanca Natl Sch Elect & Mech Lab Adv Res Ind & Logist Engn Casablanca 20202 Morocco

This paper presents an optimization approach for supply chain management that incorporates goal programming (GP), dependent chance constraints (DCC), and the hunger games search algorithm (HGSA). The model acknowledges uncertainty by embedding uncertain parameters that promote resilience and efficiency. It focuses on minimizing costs while maximizing on-time deliveries and optimizing key decision variables such as production setups, quantities, inventory levels, and backorders. Extensive simulations and numerical results confirm the model's effectiveness in providing robust solutions to dynamically changing supply chain problems when compared to conventional models. However, the integrated model introduces substantial computational complexity, which may pose challenges in large-scale real-world applications. Additionally, the model's reliance on precise probabilistic and fuzzy parameters may limit its applicability in environments with insufficient or imprecise data. Despite these limitations, the proposed approach has the potential to significantly enhance supply chain resilience and efficiency, offering valuable insights for both academia and industry.

关键词： supply chain management goal programming dependent chance constraints hunger games search algorithm uncertainty uncertain parameters cost minimization on-time delivery

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