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Supercell thunderstorm algorithm (STA): a nature-inspired metaheuristic algorithm for engineering optimization

Neural Computing and Applications 2025年第10期37卷 7207-7260页

作者： Hassan, Mohamed H. Kamel, Salah Ministry of Electricity and Renewable Energy Cairo Egypt Department of Electrical Engineering Faculty of Engineering Aswan University Aswan81542 Egypt

In this paper, an optimization algorithm called supercell thunderstorm algorithm (STA) is proposed. STA draws inspiration from the strategies employed by storms, such as spiral motion, tornado formation, and the jet stream. It is a computational algorithm specifically designed to simulate and model the behavior of supercell thunderstorms. These storms are known for their rotating updrafts, strong wind shear, and potential for generating tornadoes. The optimization procedures of the STA algorithm are based on three distinct approaches: exploring a divergent search space using spiral motion, exploiting a convergent search space through tornado formation, and navigating through the search space with the aid of the jet stream. To evaluate the effectiveness of the proposed STA algorithm in achieving optimal solutions for various optimization problems, a series of test sequences were conducted. Initially, the algorithm was tested on a set of 23 well-established functions. Subsequently, the algorithm’s performance was assessed on more complex problems, including ten CEC2019 test functions, in the second experimental sequence. Finally, the algorithm was applied to five real-world engineering problems to validate its effectiveness. The experimental results of the STA algorithm were compared to those of contemporary metaheuristic methods. The analysis clearly demonstrates that the developed STA algorithm outperforms other methods in terms of performance. © The Author(s) 2025.

关键词： optimization algorithms

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An Enhanced Team-Oriented Swarm optimization Algorithm (ETOSO) for Robust and Efficient High-Dimensional Search

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BIOMIMETICS 2025年第4期10卷 222-222页

作者： BenAbdennour, Adel Islamic Univ Madinah Coll Engn Madinah 42351 Saudi Arabia

This paper introduces the Enhanced Team-Oriented Swarm optimization (ETOSO) algorithm, a novel refinement of the Team-Oriented Swarm optimization (TOSO) algorithm aimed at addressing the stagnation problem commonly encountered in nature-inspired optimization approaches. ETOSO enhances TOSO by integrating innovative strategies for exploration and exploitation, resulting in a simplified algorithm that demonstrates superior performance across a broad spectrum of benchmark functions, particularly in high-dimensional search spaces. A comprehensive comparative evaluation and statistical tests against 26 established nature-inspired optimization algorithms (NIOAs) across 15 benchmark functions and dimensions (D = 2, 5, 10, 30, 50, 100, 200) confirm ETOSO's superiority relative to solution accuracy, convergence speed, computational complexity, and consistency.

关键词： enhanced team-oriented swarm optimization nature-inspired algorithms optimization algorithms high-dimensional search spaces exploration and exploitation benchmarks

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Knowledge-based optimization in epidemics prevention

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Soft Computing 2025年第2期29卷 875-893页

作者： Michalak, Krzysztof Department of Information Technologies Wroclaw University of Economics and Business Wroclaw Poland

In this paper, a method for knowledge-based optimization of vaccination assignments is proposed, which combines multiobjective optimization algorithms with counter-epidemic strategies known from epidemiology. In the paper, a model based on real-life illness costs is used, which allows taking into account the age of individuals exposed to a simulated epidemic. Using this model, strategies based on the age and on the graph node degree (the number of contacts each individual has) are studied. The optimization algorithms work on a graph which represents a "known" network of contacts and the solutions are subsequently used for controlling an epidemic spreading on another graph representing an "actual" network of contacts. The optimized solutions are tested on "actual" graphs with a varying degree of overlap with the "known" graph on which the optimization algorithms work. In the experiments, the age-based strategy was found to perform best, and the degree-based strategy turned out to be the second-best approach. Both these strategies outperformed other approaches not using knowledge from epidemiology to improve optimization results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： optimization algorithms

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Groupers and moray eels (GME) optimization: a nature-inspired metaheuristic algorithm for solving complex engineering problems

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Neural Computing and Applications 2025年第1期37卷 63-90页

作者： Mansour, Nehal A. Saraya, M. Sabry Saleh, Ahmed I. Artificial Intelligence Lab Nile Higher Institute for Engineering and Technology Mansoura Egypt Computers and Control Department Faculty of Engineering Mansoura University Mansoura Egypt

As engineering technology advances and the number of complex engineering problems increases, there is a growing need to expand the abundance of swarm intelligence algorithms and enhance their performance. It is crucial to develop, assess, and hybridize new powerful algorithms that can be used to deal with optimization issues in different fields. This paper proposes a novel nature-inspired algorithm, namely the Groupers and Moray Eels (GME) optimization algorithm, for solving various optimization problems. GME mimics the associative hunting between groupers and moray eels. Many species, including chimpanzees and lions, have shown cooperation during hunting. Cooperative hunting among animals of different species, which is called associative hunting, is extremely rare. Groupers and moray eels have complementary hunting approaches. Cooperation is thus mutually beneficial because it increases the likelihood of both species successfully capturing prey. The two predators have complementary hunting methods when they work together, and an associated hunt creates a multi-predator attack that is difficult to evade. This example of hunting differs from that of groups of animals of the same species due to the high level of coordination among the two species. GME consists of four phases: primary search, pair association, encircling or extended search, and attacking and catching. The behavior characteristics are mathematically represented to allow for an adequate balance between GME exploitation and exploration. Experimental results indicate that the GME outperforms competing algorithms in terms of accuracy, execution time, convergence rate, and the ability to locate all or the majority of local or global optima. © The Author(s) 2024.

关键词： optimization algorithms

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Distributed Online optimization Of Consensus-Based Adaptive Gradients Over Time-Varying Networks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 190-197页

作者： Sheng, Qingyang Shi, Xiasheng Su, Yanxu Anhui University School of Artificial Intelligence Hefei230601 China Ministry of Education Engineering Research Center of Autonomous Unmanned System Technology Hefei230601 China

The application of distributed optimization to ICPSs has advantages and challenges. Recently, a consensus-based distributed adaptive moment estimation method, referred to as DAdam (Distributed Adam), has been proposed as a variant of Adam specifically tailored for distributed and parallel computing environments. DAdam integrates the benefits of adaptive learning rate and moment estimation, but its application scenarios are limited. The assumption of static networks in existing literatures is conservative in real environments. To overcome this limitation, we propose DAdam-TV which can solve the optimization problems in time-varying networks. After rigorous analysis, we exam the convergence of proposed algorithm. For convex and non-convex problems, we bound the dynamic regret and local regret, respectively. Numerical simulations show that DAdam-TV has better performance in solving optimization problems in dynamic networks. DAdam-TV breaks through the limitation of static application scenarios, which makes the algorithm more general and effective in practical applications such as ICPSs. © 2023 IEEE.

关键词： optimization algorithms

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Secure Clustering and Routing Based on Modified Whale optimization Algorithm in MANET

Secure Clustering and Routing Based on Modified Whale Optimi...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Ravi Kiran Varma, P. Kumar, Bura Vijay Hemalatha, K.L. Jabar, Muhammed Basim Manjula, B.M. Department of CSE Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India National University of Science and Technology Dhi Qar Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Mobile Ad Hoc Networks (MANETs) are a self-organizing and adaptive technique comprising several mobile nodes. These networks’ dynamic and changeable architecture creates substantial issues in terms of trust and security for wireless communication lines. To solve these concerns, the Modified Whale optimization Algorithm (MWOA) is used to provide a secure clustering and routing solution in MANET. The Modified K-means (MKM) method is used in the Cluster Head (CH) selection process to provide effective transmission to the target node without packet loss. MWOA, an optimized technique, is introduced to improve MANET routing security. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network Lifetime (NL), and end-to-end delay (EED) are used to evaluate the suggested methodology. Comparative investigation shows that the MWOA methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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An Adaptive Golden Jackal optimization Algorithm for Mobile Robotic Path Planning

An Adaptive Golden Jackal Optimization Algorithm for Mobile ...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Given the shortcomings of the traditional Golden Jackal optimization (GJO) algorithm, including limited accuracy and slow convergence speed in solving mobile robot path planning problems, an improved adaptive Golden Jackal optimization (IAGJO) method is proposed. Firstly, a nonlinear adaptive energy strategy is presented to adjust the energy decay pattern, balancing global and local search. Secondly, an enhanced position update mechanism based on Cauchy and Gaussian mutation increases population diversity and guide the search based on optimal individuals, thereby facilitating efficient exploration of unknown regions and avoiding local optima. Finally, the IAGJO algorithm is applied to mobile robot path planning (MRPP), demonstrating that the IAGJO achieves shorter path lengths and higher search efficiency, exhibiting significant advantages over existing methods. © 2025 SPIE.

关键词： optimization algorithms

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Research on the Application of Information Sharing Technology in Distributed Photovoltaic Aggregation optimization Control Strategy 5

Research on the Application of Information Sharing Technolog...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Ling, Daiwei Huang, Kailang Wang, Kai Kong, Detian Zhou, Ran Wanning Power Supply Bureau of Hainan Power Grid Co. Ltd Wanning China

ISBN: (纸本)9798331533694

This paper studies the application of information sharing technology in distributed photovoltaic aggregation optimization control, and proposes an optimization control strategy based on distributed gradient descent algorithm. This strategy realizes collaborative control between each substation through information sharing technology to reduce power fluctuations and improve the overall efficiency of photovoltaic power generation. This paper constructs a mathematical model of distributed photovoltaic system and designs a corresponding distributed gradient descent algorithm, which can realize global optimization control under the premise of exchanging only a small amount of information between each substation. The effectiveness of the optimization control strategy is verified by modeling and testing the photovoltaic system data of a certain actual substation. The simulation results show that after applying the distributed gradient descent algorithm, the information sharing between each substation improves the overall power generation efficiency of the system and reduces power fluctuations. Specific data show that without increasing additional hardware costs, the power generation efficiency of the system is increased by about 8% and the power fluctuation is reduced by about 12%. These results fully demonstrate that the proposed distributed photovoltaic optimization control strategy based on information sharing has significant advantages in practical applications. © 2025 IEEE.

关键词： optimization algorithms

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The Three-Point optimization Algorithm: A Novel Physics-Based Metaheuristic Approach 1st

The Three-Point Optimization Algorithm: A Novel Physics-Base...

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1st International Symposium on Software Fault Prevention, Verification, and Validation, SFPVV 2024

作者： Deng, Xiong Liu, Shaoying Liu, Yanli Hiroshima University Hiroshima Higashi-Hiroshima City739-8511 Japan Anyang Normal University Henan Anyang City455000 China

ISBN: (纸本)9789819616206

In this paper, a novel metaheuristic approach called The Three-point optimization Algorithm (TPOA) is proposed. TPOA is inspired by the principle of three point positioning. Three point positioning is a geometric based positioning method that determines the position of a target by measuring the distance between the target point and a known point. This method has a wide range of applications in multiple fields, including geological surveying, satellite navigation systems, and WeChat positioning. TPOAs performance is demonstrated by benchmarking with 10 well-known test functions (including unimodal, multimodal, fixed-dimension multimodal, and composite functions). Moreover, TPOAs results are verified by comparison to 5 other metaheuristics. In this study, the experimental results for the above 6 methods on the 10 testing functions in terms of the Mean (mean value), Std (standard deviation), the Best(the best value), the Worst(the worst value), and Burden(cost time), and it suggest that TPOAs results are competitive and, in many instances, outperform the aforementioned well-known metaheuristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Logistics Distribution Path Intelligent optimization System Based on Floyd optimization Algorithm 14th

Logistics Distribution Path Intelligent Optimization System ...

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14th International Conference on Frontier Computing, FC 2024

作者： Shi, Danni Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9789819623907

The article analyzed the construction of an intelligent optimization system for rural logistics distribution paths. The overall design concept and technical framework of the intelligent optimization system for rural logistics distribution paths were proposed, and the specific functions and implementation methods of the intelligent optimization system for rural logistics distribution paths were introduced. Finally, through experiments on specific examples, the average delivery cost of A Express Company was reduced to 59000 yuan, and the delivery time of B Express Company was shortened to 14.8 h. This verified that the system has significant advantages in reducing rural logistics delivery costs and improving delivery efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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