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New buzzard optimization algorithm for multi-level thresholding and comparison with multi-agent based fuzzy particle swarm optimization algorithm

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Multimedia Tools and Applications 2025年 1-18页

作者： Arshaghi, Ali Department of Electrical Engineering Central Tehran Branch Islamic Azad University Tehran Iran

In this article, a new optimal multi-level thresholding algorithm for histogram-based image segmentation present. The proposed algorithm is an improved type of particle optimization algorithm. In this paper, the selection of image Thresholding done using a new method called Buzzard optimization Algorithm (BUZO). Each particle (Buzzard) has two vectors in the search space, one is the ability vector, and the other is the position vector. The new position depends on these two vectors. The proposed BUZO algorithm has also solve these weaknesses of being stuck in local optimal points and early convergence and compensates for the failings of other algorithms. PSNR compares the proposed BUZO algorithm with PSO and MAFPSO algorithms as follows: In the image of the tree for the PSNR test of the BUZO algorithm in 5-level thresholding, it is 29.8, which is more different from the PSNR of the PSO and MAFPSO algorithms, which are 25 and 28, respectively. In addition, in another pirate test image, the PSNR of the BUZO algorithm in 5-level thresholding is 28.3, compared to the PSNR of PSO and MAFPSO algorithms, which are 25 and 26, respectively. In addition, in the pirate test image, the PSNR value of the BUZO algorithm in 2-level thresholding is 22.9, which compared to the PSNR of PSO algorithm 18 and MAFPSO algorithm 22.3. These results show the superiority of this algorithm with other methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： optimization algorithms

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Enhanced genetic algorithm for indoor low-illumination stereo matching energy function optimization

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Alexandria Engineering Journal 2025年 121卷 1-17页

作者： Hongjin, Zhang Hui, Wei Laboratory of Algorithms for Cognitive Models School of Computer Science Fudan University Handan Road 220 ShangHai China College of Engineering Science and Technology Shanghai Ocean University Huchenghuan Road 999 ShangHai China

This study presents an enhanced genetic algorithm to generate high-quality, dense disparity maps in low-illumination indoor stereo matching scenarios. The algorithm utilizes a novel biomimetic mutation strategy, dynamically adjusted through common allelic theory, to effectively reduce the search space and improve optimization efficiency. A multi-objective genetic approach is employed, optimizing both data fidelity and smoothness to achieve balanced and accurate results. The Integration of biological evolution principles, including a hierarchical performance index system, ensures the algorithm balances local and global features during optimization. Experimental results demonstrate that the proposed algorithm consistently converges within 10 iterations, reducing average disparity errors by 33.7% compared to state-of-the-art methods and by 20% compared to a control group. Additionally, the algorithm achieves a lower computational complexity of O(H), offering significant improvements in resource efficiency compared to standard algorithms. These findings validate the algorithm's reliability, advancement, and effectiveness for stereo matching under challenging conditions. © 2025 The Authors

关键词： optimization algorithms

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New comparative approach to multi-level thresholding: chaotically initialized adaptive meta-heuristic optimization methods

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Neural Computing and Applications 2025年第14期37卷 8371-8396页

作者： Serbet, Fatmanur Kaya, Turgay Department of Electrical and Energy Pertek Sakine Genc Vocational School Munzur University Tunceli62000 Turkey Department of Electrical-Electronics Engineering Faculty of Engineering Firat University Elazig23119 Turkey

One method aimed at enhancing the performance of meta-heuristic optimization techniques is the incorporation of chaotic systems. Instead of irregular distributions in the search space, chaotic distributions are employed in the initial population of optimization algorithms to improve the efficiency of the search process. This approach enables search agents distributed in a chaotic manner to effectively explore the search space. The initial populations of both the well-established PSO algorithm and the enhanced WSO algorithm, which incorporates advanced search techniques, are distributed in the search space according to the characteristics of Logistic, Chebyshev, Circle, Sine, and Piecewise chaotic maps in this study. The original PSO and WSO algorithms, as well as the resulting chaotically initialized PSO and chaotically initialized WSO algorithms, were tested using 23 benchmark functions. Subsequently, the Otsu method was integrated into the tested optimization algorithms to obtain multi-level thresholding values. These algorithms were applied to five different test images with a manually determined number of thresholds. The results obtained were presented in the study and evaluated using statistical tests. © The Author(s) 2025.

关键词： optimization algorithms

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WHALE optimization ALGORITHM FOR EFFICIENT TASK ALLOCATION IN THE INTERNET OF THINGS

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Scalable Computing 2025年第3期26卷 1412-1421页

作者： Shu, Wanchang Jiangxi Teachers College Jiangxi Yingtan335000 China

In order to solve the problem of reducing worker costs and improving worker efficiency, the author proposes a title whale optimization algorithm for efficient task allocation in the Internet of Things. This algorithm adopts the fuzzy chance constrained programming method to model the online time of workers, and introduces delay costs and idle costs based on whether there is delay or not, Due to the fact that the corresponding problem is a combinatorial optimization problem and belongs to the NP hard problem category, a two-stage task allocation algorithm is designed to solve it in combination with the whale optimization algorithm. The experimental results show that after being simulated by the algorithm, half of the workers reached the highest efficiency of 1, and the expected online time of the workers was less than 30, and the task execution time of the workers was less than 35. The task allocation algorithm designed by the author has higher worker efficiency compared to other algorithms and has broad application prospects. © 2025 SCPE.

关键词： optimization algorithms

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

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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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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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Research on the Optimal Location of Urban Electric Vehicle Charging Stations Based on the Whale optimization Algorithm in the Context of Green Energy

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Distributed Generation and Alternative Energy Journal 2025年第1期40卷 193-212页

作者： Xu, Fangheng Zhang, Wen Zhejiang Industry Polytechnic College Zhejiang Shaoxing312000 China State Grid Corporation of China Shaoxing Power Supply Company Zhejiang 312000 China

With the gradual increase in the number of new energy vehicles in cities, the current location and number of urban charging piles have seriously affected the residents’ demand for charging due to the limitations of urban population and urban roads. Based on the perspective of economic operation and user experience, a charging pile location model based on charging pile maintenance cost and user satisfaction is proposed, and the whale optimisation algorithm is used to seek the optimal location. Since the whale optimization algorithm has the disadvantage of fast convergence and easy to fall into local optimum, we propose a new algorithm based on Tent population initialization and adaptive factor optimization-Tent Adapt Whale optimization Alogrithm (TAWOA)-to improve the performance of the algorithm as a whole, and in the simulation experiments, the performance of TAWOA is improved compared with that of ACO, PSO, and WOA in the three benchmark functions, and in the charging pile siting, TAWOA algorithm is able to obtain better results than WOA, which can effectively reduce the number of charging piles and reduce energy consumption. © 2025 River Publishers.

关键词： optimization algorithms

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Robust Pose Graph optimization Against Outliers Using Consistency Credibility Factor

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 1044-1046页

作者： Cai, Jie Wei, Guoliang Li, Wangyan Wang, Yaolei College of Science University of Shanghai for Science and Technology Shanghai200093 China Business School University of Shanghai for Science and Technology Shanghai200093 China

Dear Editor, Pose graph optimization (PGO) is a popular optimization approach that plays a crucial role in the simultaneous localization and mapping (SLAM) back-end. However, when incorrect loop closure constraints (referred to as outliers) are present in the SLAM front-end, the standard PGO algorithm fails catastrophically and can not return an accurate map. To address this issue, this letter proposes a novel algorithm that leverages classical optimization methods to effectively handle outliers. The proposed algorithm introduces a new formulation that incorporates a credibility factor model, which improves the robustness of the optimization process. Additionally, an innovative consistency classification algorithm is developed to detect outliers. Extensive experiments are conducted on multiple benchmark datasets to evaluate the consistency and accuracy of the proposed algorithm. The results demonstrate that the proposed algorithm surpasses several existing studies in terms of the weighted summation of squared measurement errors (i.e., denoted by $E_{G}^optimization$), highlighting its superior performance. Overall, the proposed algorithm presents a promising solution to mitigate the adverse effects of outliers in PGO, thereby enhancing the accuracy and reliability of the SLAM process. © 2014 Chinese Association of Automation.

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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