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Golden eagle optimizer: A nature-inspired metaheuristic algorithm

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 152卷 107050-107050页

作者： Mohammadi-Balani, Abdolkarim Nayeri, Mahmoud Dehghan Azar, Adel Taghizadeh-Yazdi, Mohammadreza Tarbiat Modares Univ Fac Management & Econ Dept Ind Management Jalal Al Ahmad HighwayPOB 14115-111 Tehran *** Iran Univ Tehran Fac Management Dept Ind Management Tehran Iran

This paper proposes a nature-inspired swarm-based metaheuristic for solving global optimization problems called Golden Eagle Optimizer (GEO). The core inspiration of GEO is the intelligence of golden eagles in tuning speed at different stages of their spiral trajectory for hunting. They show more propensity to cruise around and search for prey in the initial stages of hunting and more propensity to attack in the final stages. A golden eagle adjusts these two components to catch the best possible prey in feasible region the shortest possible time. This behavior is mathematically modeled to highlight exploration and exploitation for a global optimization method. The performance of the proposed algorithm is tested and confirmed using 33 benchmark test functions and a scalability test. Results were compared to that of six other well-known algorithms, which revealed GEO's superiority, which indicates that it can find the global optimum and avoid local optima effectively. The Multi-Objective Golden Eagle Optimizer (MOGEO) is also proposed to solve multi-objective problems. The performance of MOGEO is also tested and verified on ten multi-objective benchmark functions. Results were compared to that of two other multi-objective algorithms, which showed that it can approximate true Pareto optimal solutions better than the other two algorithms. The software (toolbox) and source code for GEO and MOGEO are also provided, which are publicly available.

关键词： Golden eagle optimizer Multi-objective golden eagle optimizer Nature-inspired computing Swarm intelligence metaheuristic algorithm

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Multi-satellite cooperative scheduling method for large-scale tasks based on hybrid graph neural network and metaheuristic algorithm

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ADVANCED ENGINEERING INFORMATICS 2024年 60卷

作者： Feng, Xiaoen Li, Yuqing Xu, Minqiang Harbin Inst Technol Deep Space Explorat Res Ctr Harbin 150001 Peoples R China

Traditional heuristic optimization algorithms are no longer applicable to the multiple satellites scheduling with large-scale tasks, as they are unable to provide satisfactory performance in terms of convergence and speed when addressing complex constraint conflicts. Therefore, we propose a multi-satellite cooperative scheduling method for large-scale tasks based on a hybrid graph neural network (GNN) and metaheuristic algorithm. By using the representation and extraction capability of GNN for relations in graph, the features of large-scale tasks and their constraint relations were expressed, and the generalized knowledge was extracted. And combined with metaheuristic algorithm, the optimization framework for large-scale satellite task scheduling based knowledge and data was implemented. The method consists of a GNN pre-solver module for static constraint conflicts and a metaheuristic optimization module for dynamic constraint conflicts of satellite missions. In the GNN pre-solver module, the graph sample and aggregate network was used to learn and extract the common knowledge in the static task-conflict graph, and provided effective prior information for the metaheuristic optimization module. The quadratic unconstrained binary optimization loss function was designed with multiple influencing factors, to convert the discrete optimization function into a continuous function;a greedy threshold was also added to improve the task completion rate. Finally, numerical experimental results showed that the proposed method can achieve an efficient solution to the multi-satellite scheduling problem with tens of thousands tasks. Compared with the commonly used multi-satellite scheduling algorithms (the genetic algorithm (GA) and greedy-GA), the proposed method can obtain higher-quality solutions under the same conditions and greatly improve the computational efficiency of large-scale mission planning.

关键词： Earth observation satellites Large-scale satellite missions Multi-satellite scheduling Graph neural network metaheuristic algorithm

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A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

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ENERGY 2024年 295卷

作者： Liu, Jiangchuan Ma, Qixin Zhang, Quanchang Hunan Univ Res Ctr Adv Powertrain Technol Dept Energy & Power Engn Changsha 410082 Peoples R China

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle ' s lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil -cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil -cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

关键词： Energy management Oil -cooled motor MPC LSTM metaheuristic algorithm

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Energy-efficient routing protocol for underwater wireless sensor networks using a hybrid metaheuristic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartC期133卷

作者： Saemi, Behzad Goodarzian, Fariba Kavosh Inst Higher Educ Comp Dept Mahmood Abad Mazandaran Iran Heriot Watt Univ Edinburgh Business Sch EBS Riccarton EH14 4AS Currie Scotland Heriot Watt Univ Sch Social Sci Riccarton EH14 4AS Currie Scotland

Energy-efficient routing protocols for Underwater Wireless Sensor Networks (UWSNs) have become critical in recent years for the intelligent and reliable collection of data from the seas and oceans. UWSNs are a group of deep-water sensors that are used for marine exploration and ocean surveillance. This network is used to route data collected by sensors deployed at different water depths to surface water sensors (sinks). Transmitting the collected data from the ocean's depths to the surface is difficult due to the limited available bandwidth, inconvenient location, high mobility of the sensors, and, most importantly, limited energy. The purpose of this paper is to present a routing protocol that improves the reliability of data transmission from a source sensor to a destination sensor. A hybrid metaheuristic algorithm called GSLS is proposed to use in this protocol, which combines a Global Search algorithm (GSA) and a Local Search algorithm (LSA). The proposed GSA is an algorithm inspired by several of the Genetic algorithm's (GAs) solution updating properties. The proposed LSA algorithm is an extension of the scattered search algorithm. The proposed GSA and LSA are combined in parallel to search the problem's space simultaneously to find an optimal path in an acceptable time. as a result, more problem area is examined, and the algorithm's run time to find the best route is reduced. Our simulation results emphasize the high quality of the path, the algorithm's low energy consumption, and the algorithm's high speed in comparison to the state-of-the-art.

关键词： Energy -efficient Underwater wireless sensor networks Routing metaheuristic algorithm Genetic algorithm

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Groundwater vulnerability mapping using the modified DRASTIC model: the metaheuristic algorithm approach

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2021年第1期193卷 25-25页

作者： Balaji, L. Saravanan, R. Saravanan, K. Sreemanthrarupini, N. A. Anna Univ Ctr Water Resources Chennai 600025 India

Vulnerability assessment and mapping is a significant tool for sustainable management of the precious natural groundwater resources. DRASTIC is an extensively used index model to map groundwater vulnerable zones. However, the original DRASTIC model rates and weights used in most of the research depict the poor correlation between nitrate concentration and groundwater vulnerability index. Wilcoxon test and five population-based metaheuristic (MH) algorithms, namely, firefly algorithm (FA), invasive weed optimization (IWO), teaching learning-based optimization (TLBO), shuffled frog leaping algorithm (SFLA), and particle swarm optimization (PSO), were used to optimize the rates and weights of the DRASTIC model to improve its accuracy. The performance of all the employed metaheuristic algorithms converges to a global optimal solution at different iterations, and to choose the best algorithm for DRASTIC weights optimization, a ranking methodology was proposed. The algorithms were ranked by calculating the relative closeness of alternatives with computational speed and the number of iterations as attributes in the TOPSIS method. This study identifies FA as the outperforming algorithm among the employed for this specified weight optimization problem based on ranking. The result of the optimization model proposed depicts significant improvement in the correlation coefficient between the groundwater vulnerability index and nitrate concentration from 0.0545 for the original DRASTIC model to 0.7247 for the Wilcoxon-MH- DRASTIC. Hence, this ranking approach can be adopted when global optimal solution is found by all employed algorithms in DRASTIC weight optimization.

关键词： Groundwater vulnerability DRASTIC model Wilcoxon test metaheuristic algorithm TOPSIS

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Optimizing Hyperparameters for Improved Melanoma Classification using metaheuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 531-540页

作者： Adamu, Shamsuddeen Alhussian, Hitham Aziz, Norshakirah Abdulkadir, Said Jadid Alwadin, Ayed Imam, Abdullahi Abubakar Garba, Aliyu Saidu, Yahaya Univ Teknol PETRONAS Comp & Informat Sci Dept Seri Iskandar Malaysia Ahmadu Bello Univ IAIICT Zaria Nigeria Univ Brunei Darussalam UBD Brunei Gadong Brunei King Saud Univ Comp Sci Dept Community Coll Riyadh Saudi Arabia

Melanoma a prevalent and formidable skin cancer, necessitates early detection for improved survival rates. The rising incidence of melanoma poses significant challenges to healthcare systems worldwide. While deep neural networks offer the potential for precise melanoma classification, the optimization of hyperparameters remains a major obstacle. This paper introduces a groundbreaking approach that harnesses the Manta Rays Foraging Optimizer (MRFO) to empower melanoma classification. MRFO efficiently fine-tunes hyperparameters for a Convolutional Neural Network (CNN) using the ISIC 2019 dataset, which comprises 776 images (438 melanoma, 338 non -melanoma). The proposed cost-effective DenseNet121 model surpasses other optimization methods in various metrics during training, testing, and validation. It achieves an impressive accuracy of 99.26%, an AUC of 99.56%, an F1 score of 0.9091, a precision of 94.06%, and a recall of 87.96%. Comparative analysis with EfficientB1, EfficientB7, EfficientNetV2B0, NesNetLarge, ResNet50, VGG16, and VGG19 models demonstrates its superiority. These findings underscore the potential of the novel MRFO-based approach in achieving superior accuracy for melanoma classification. The proposed method has the potential to be a valuable tool for early detection and improved patient outcomes.

关键词： Deep learning machine learning classification metaheuristic algorithm CNN

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Prey-Predator algorithm: A New metaheuristic algorithm for Optimization Problems

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2015年第6期14卷 1331-1352页

作者： Tilahun, Surafel Luleseged Ong, Hong Choon Univ Addis Ababa Fac Sci Computat Sci Program Addis Ababa 1176 Ethiopia Univ Sains Malaysia Sch Math Sci Usm 11800 Pulau Pinang Malaysia

Nature-inspired optimization algorithms have become useful in solving diffcult optimization problems in different disciplines. Since the introduction of evolutionary algorithms several studies have been conducted on the development of metaheuristic optimization algorithms. Most of these algorithms are inspired by biological phenomenon. In this paper, we introduce a new algorithm inspired by prey-predator interaction of animals. In the algorithm randomly generated solutions are assigned as a predator and preys depending on their performance on the objective function. Their performance can be expressed numerically and is called the survival value. A prey will run towards the pack of preys with better surviving values and away from the predator. The predator chases the prey with the smallest survival value. However, the best prey or the prey with the best survival value performs a local search. Hence the best prey focuses fully on exploitation while the other solution members focus on the exploration of the solution space. The algorithm is tested on selected well-known test problems and a comparison is also done between our algorithm, genetic algorithm and particle swarm optimization. From the simulation result, it is shown that on the selected test problems prey-predator algorithm performs better in achieving the optimal value.

关键词： metaheuristic algorithm prey-predator algorithm (PPA) optimization bio-inspired algorithms

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Exploring the Impact of Time Management Skills on Academic Achievement with an XGBC Model and metaheuristic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 107-119页

作者： Li, Songyang Criminal Invest Police Univ China Police Skills & Tact Training Dept Shenyang 110035 Liaoning Peoples R China

Estimating a student's academic performance is a crucial aspect of learning preparation. In order to predict understudy academic performance, this consideration uses a few Machine Learning (ML) models and Time Administration Aptitudes data from the Time Structure Questionnaire (TSQ). While a number of other useful characteristics have been used to forecast academic achievement, TSQ findings, which directly evaluate students' time management skills, have never been included. This oversight is surprising, as time management skills likely play a significant role in academic success. Time administration may be an ability that may impact the student's academic accomplishment. The purpose of this research is to look at the connection between college students' academic success and their ability to manage their time well. The Extreme Gradient Boosting Classification (XGBC) model has been utilized in this study to forecast academic student performance. To enhance the prediction accuracy of the XGBC model, this study employed three optimizers: Giant Trevally Optimizer (GTO), Bald Eagle Search Optimization (BESO), and Seagull Optimization algorithm (SOA). Impartial performance evaluators were employed in this study to assess the models' predictions, minimizing potential biases. The findings showcase the success of this approach in developing an accurate predictive model for student academic performance. Notably, the XGBE surpassed other models, achieving impressive accuracy and precision values of 0.920 and 0.923 during the training phase.

关键词： Student academic performance time management machine learning extreme gradient boosting classification metaheuristic algorithm

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ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

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SOFT COMPUTING 2023年第18期27卷 13553-13581页

作者： Mostafa, Reham R. El-Attar, Noha E. Sabbeh, Sahar F. Vidyarthi, Ankit Hashim, Fatma A. Mansoura Univ Informat Syst Dept Fac Comp & Informat Sci Mansoura 35516 Egypt Benha Univ Fac Comp & Artificial Intelligence Banha Egypt Univ Jeddah Coll Comp Sci & Engn Jeddah Saudi Arabia Jaypee Inst Informat Technol Dept CSE & IT Noida India Helwan Univ Fac Engn Helwan Egypt

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration-exploitation stages during the search process;moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the p

关键词： Sooty tern optimization Ant lion optimization Feature optimization metaheuristic algorithm High dimensional search space

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Ensemble of metaheuristic and Exact algorithm Based on the Divide-and-Conquer Framework for Multisatellite Observation Scheduling

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第5期58卷 4396-4408页

作者： Wu, Guohua Luo, Qizhang Du, Xiao Chen, Yingguo Suganthan, Ponnuthurai Nagaratnam Wang, Xinwei Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 119260 Singapore Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar Delft Univ Technol Dept Transport & Planning NL-2628 CN Delft Netherlands

Satellite observation scheduling plays a significant role in improving the efficiency of Earth observation systems. To solve the large-scale multisatellite observation scheduling problem, this article proposes an ensemble of metaheuristic and exact algorithms based on a divide-and-conquer framework (EHE-DCF), including a task allocation phase and a task scheduling phase. In the task allocation phase, each task is allocated to a proper orbit based on a metaheuristic incorporated with a probabilistic selection and a tabu mechanism derived from ant colony optimization and tabu search, respectively. In the task scheduling phase, we construct a task scheduling model for every single orbit and solve the model by using an exact method (i.e., branch and bound, B&B). The task allocation and task scheduling phases are performed iteratively to obtain a promising solution. To validate the performance of the EHE-DCF, we compare it with B&B, three divide-and-conquer-based metaheuristics, and a state-of-the-art metaheuristic. Experimental results show that the EHE-DCF can obtain higher scheduling profits and complete more tasks compared with existing algorithms. The EHE-DCF is especially efficient for large-scale satellite observation scheduling problems.

关键词： Task analysis Earth Observing System Satellites Orbits Processor scheduling Resource management Optimal scheduling Divide-and-conquer framework (DCF) ensemble algorithm exact algorithm metaheuristic algorithm observation satellite scheduling

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