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Multi-threshold medical image segmentation based on the enhanced walrus optimizer

JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-41页

作者： Li, Jie Lu, Ruicheng Zeng, Biqing Zhang, Jinzhong Deng, Yuhui Feng, Hao South China Normal Univ Sch Artificial Intelligence Foshan 588225 Guangdong Peoples R China West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Anhui Peoples R China Jinan Univ Dept Comp Sci Guangzhou 510632 Guangdong Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570000 Hainan Peoples R China

The image segmentation method is crucial for disease diagnosis as it provides radiologists with important patient information in a non-invasive form. The adopted image segmentation method significantly impacts diagnostic efficiency and accuracy. Multi-level thresholding is extensively utilized in medical image segmentation owing to its effectiveness and computational simplicity. Conventional techniques, such as Otsu's method for maximizing between-class variance, have shown effectiveness in determining optimal thresholds. However, their application in scenarios requiring more than two thresholds can lead to high computational demands. Although meta-heuristic algorithms are often used for threshold determination, they frequently encounter challenges such as inaccurate convergence, a propensity to settle in local optima, and increased sensitivity as the number of thresholds rises. To tackle these challenges, we introduce a novel algorithm called the enhanced Walrus optimizer (EWO). EWO is crafted for tasks involving image segmentation and global optimization, with the objective of enhancing both accuracy and efficiency in selecting thresholds for medical image segmentation. The algorithm employs opposition-based learning alongside a tridistribution optimal mutation strategy that harnesses three different distribution functions (L & eacute;vy, Gaussian, and Cauchy) to mutate search agents. This methodology boosts the quality of solutions and improves the diversity of search agents. We further conduct experimental comparisons of the proposed EWO algorithm against six benchmark algorithms on MRI brain images, employing evaluation methods including objective function values, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM). EWO achieved superior performance, obtaining the best mean objective function values in 78.57% (22 out of 28) of the cases and the lowest standard deviation of objective function values in 60.71

关键词： Image segmentation meta-heuristic algorithm Optimal thresholding L & eacute vy distribution

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Prefabricated beam-slab structure optimization based on multi-layer graphical representation and genetic-RAO algorithm

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ADVANCED ENGINEERING INFORMATICS 2025年 64卷

作者： Yang, Zhexi Lu, Wei-Zhen City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China

The beam-slab structure optimization problem is a complex large-scale combinatorial problem. Traditionally, structural design has heavily relied on engineers' experience using trial-and-error methods, while optimization algorithms require complex parameter tuning, which deviates from practical engineering needs. To bridge this gap, this study proposes an innovative workflow integrating a Genetic-RAO algorithm with Building Information Modeling to enhance the structural optimization process. The proposed workflow employs a multi-layer graph data structure to model the beam-slab structures, mapping all optimization variables to the edges of the graph, thereby enabling efficient manipulation of layout, load transfers, and component properties of structural schemes. The proposed algorithm eliminates the need for complex parameter tuning, enhancing accessibility and adaptability for practical engineering applications. Computational experiments and case studies demonstrate the proposed algorithm achieves cost reductions of 8.69%, 16.14%, 12.07% and 6.42% across four floor plans compared to the conventional genetic algorithm. Additionally, automating result visualization within the Revit platform promotes subsequent design modifications and fosters multi-disciplinary collaboration. These findings indicate that the proposed workflow significantly improves cost-effectiveness and efficiency in structural engineering optimization, offering a practical solution for the industry.

关键词： Beam-slab structure Structural optimization meta-heuristic algorithm RAO algorithm Building information modeling

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Elite-based butterfly optimization algorithm and its application in speckle projection technique

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Zhong, Xuxu Liang, Binbin Civil Aviat Adm China Chengdu Airborne Equipment Ctr Airworthiness Certificat Ctr Chengdu 610000 Peoples R China Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Peoples R China

In recent years, the butterfly optimization algorithm (BOA) has attracted a lot of attention because of its precise balance mechanism between exploitation and exploration. However, due to the shortcomings such as insufficient accuracy and slow convergence speed, the practical application of BOA is limited to a certain extent. This paper has two main contributions: 1) An elite-based butterfly optimization algorithm (eBOA) is proposed, which introduces an elite subgroup and gives full play to the positive role of elite elements in evolution. eBOA uses the optimal butterfly and the random elite butterfly to intervene in the global and local search operators respectively, thereby guiding the population to evolve in a more potential direction. Then, eBOA superimposes an existing parameter as a weight on the basis vector of the global search operator to speed up the convergence. 2) A preprocessing method for speckle images and disparity data that can be used in deep learning is proposed, and a novel 3D speckle reconstruction method is generated by combining eBOA with a deep network. In terms of results, comparative experiments based on 20 benchmark functions and 7 advanced meta-heuristic algorithms show that eBOA has excellent optimization capability;The comparison experiments of 5 deep networks confirm that the new reconstruction method can significantly improve the accuracy of speckle reconstruction models, and once again verify the remarkable optimization performance of eBOA.

关键词： meta-heuristic algorithm Butterfly optimization algorithm Speckle 3D reconstruction

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A two-timescale neurodynamic algorithm for optimal power flow problem of radial networks

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INFORMATION SCIENCES 2025年 700卷

作者： Li, Jin Lu, Jianquan Tang, Tianyu Lou, Jungang Southeast Univ Sch Math Nanjing 210096 Peoples R China Huzhou Univ Yangtze Delta Reg Huzhou Inst Intelligent Transpor Huzhou 313000 Peoples R China

The issue of optimal power flow (OPF) is a basic and a critical challenge within the analysis of electrical power systems. In radial networks, the OPF problem is formulated as a branch flow model with nonlinear equations, which makes conventional approaches difficult to be applied for solving such OPF problem, including one-layer neurodynamic approach. In this article, we propose a two-timescale neurodynamic approach based on recurrent neural networks (RNNs) to deal with the OPF optimization problem. The optimality and convergence of this two-timescale neurodynamic model are rigorously proved, indicating that it can converge to a local optimum. Subsequently, a collaborative neurodynamic approach with meta-heuristic algorithm, which has been proven to almost certainly converge to global optimum, is used to perform the global optimization and seek a global optimum. Finally, the two-timescale neurodynamic approach based on RNNs is applied to a 6 bus power system for validation and comparison with other methods. Simulation results demonstrate that the proposed approach can effectively achieve both local and global optimization, thereby verifying its correctness and effectiveness.

关键词： Neurodynamic optimization Optimal power flow Nonlinear equations Recurrent neural network meta-heuristic algorithm

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A novel improved structural controllability method on complex temporal networks based on temporal ACO algorithm

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INTERNATIONAL JOURNAL OF CONTROL 2025年

作者： Xia, Zhengwei Zhang, Feiyun Li, Na Arebi, Peyman Xuchang Univ Sch Elect Engn Xuchang 461000 Henan Peoples R China Natl Univ Skills NUS Dept Comp Engn Tehran *** Iran

The controllability of complex temporal networks is an area of research focused on understanding how to guide or influence the behaviour of dynamic. Structural controllability is considered as one of the most prominent network controllability methods. Structural controllability uses the maximum matching algorithm to find the minimum set of control nodes. The maximum matching algorithm on temporal networks is a class of NP-hard problems. In this paper, a novel method based on temporal ACO algorithm is proposed to solve the maximum matching problem in structural controllability. The ACO algorithm has been adapted to temporal networks. The results of implementing the proposed method on real-world datasets demonstrate that the ACO algorithm has a good performance and has converged to the optimal solution with high speed. The results demonstrate that the proposed method has higher efficiency in finding driver nodes and algorithm execution speed compared to the basic structural controllability.

关键词： Complex temporal networks network controllability meta-heuristic algorithm temporal ACO algorithm driver nodes

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A survey on meta-heuristic algorithms for the influence maximization problem in the social networks

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COMPUTING 2021年第11期103卷 2437-2477页

作者： Aghaee, Zahra Ghasemi, Mohammad Mahdi Beni, Hamid Ahmadi Bouyer, Asgarali Fatemi, Afsaneh Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan Iran Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran Shahid Madani Univ Dept Informat Technol & Commun Azarbaijan Tabriz Iran

The different communications of users in social networks play a key role in effect to each other. The effect is important when they can achieve their goals through different communications. Studying the effect of specific users on other users has been modeled on the influence maximization problem on social networks. To solve this problem, different algorithms have been proposed that each of which has attempted to improve the influence spread and running time than other algorithms. Due to the lack of a review of the meta-heuristic algorithms for the influence maximization problem so far, in this paper, we first perform a comprehensive categorize of the presented algorithms for this problem. Then according to the efficient results and significant progress of the meta-heuristic algorithms over the last few years, we describe the comparison, advantages, and disadvantages of these algorithms.

关键词： Social networks Influence maximization problem Viral marketing Influence spread meta-heuristic algorithm

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A multi-strategy improved Coati optimization algorithm for solving global optimization problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-48页

作者： Luo, Xin Yuan, Yage Fu, Youfa Huang, Haisong Wei, Jianan Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China Guizhou Equipment Mfg Digital Workshop Modeling & Guiyang 550025 Guizhou Peoples R China Chongqing Vocat & Tech Univ Mechatron Informat Engn Inst Chongqing 402760 Peoples R China

meta-heuristic algorithms have garnered significant attention from researchers due to their broad applicability in addressing complex optimization problems. The Coati Optimization algorithm (COA) is a swarm intelligence technique characterized by its simple structure and effectiveness. In fact, COA has great room for improvement in terms of balancing exploration and exploitation, falling into local optimization, iteration accuracy, etc. To address these limitations and enhance COA's robustness, we propose a multi-strategy improved Coati Optimization algorithm (MICOA). Firstly, the algorithm's structure is refined, and a novel exploration strategy is introduced to accelerate convergence in the early stages. Secondly, during the exploitation phase, Levy flights and Brownian motion strategies are incorporated to preserve population diversity and improve the algorithm's ability to escape local optima, thereby enhancing convergence accuracy. Finally, MICOA's performance is evaluated through tests on various dimensions of the CEC2017 and CEC2022 benchmark functions, and compared with other optimization algorithms. Experimental results demonstrate that MICOA outperforms other algorithms in overall performance. The successful application of MICOA to ten mechanical engineering optimization problems, Unmanned Aerial Vehicle (UAV) path planning, and COVID-19 case prediction further validates its feasibility and scalability for practical problem-solving.

关键词： meta-heuristic algorithm Improved coati optimization Engineering optimization Path planning of UAV

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A new Digital Harmony Search algorithm for optimizing Pump Scheduling in Water Distribution Networks

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WATER RESEARCH X 2025年 27卷 100300页

作者： De Paola, Francesco Pugliese, Francesco Fontana, Nicola Giugni, Maurizio Univ Naples Federico II Dept Civil Architectural & Environm Engn Via Claudio 21 I-80125 Naples Italy Univ Sannio Dept Engn Piazza Roma 21 I-82100 Benevento Italy

Pumps in Water Distribution Networks (WDNs) adequately provide effective pressure where low elevation or high head losses are detected within the system. One of the most effective strategies to ensure economic sustainability is Pump Scheduling (PS), assuring the optimization of pump management and enabling significant energy cost saving. meta-heuristic algorithms can be applied to Pump Scheduling, given their ability to provide reliable global solutions, further complemented by limited computational efforts. Nevertheless, they do not assure the achievement of the optimal global solution. In this paper, the Pump Scheduling optimization was solved by applying a modified Harmony Search (HS) algorithm, aimed at optimizing the energy consumption and the maintenance costs of pumps, by limiting the number of daily switches. The EPANET 2.0 solver was employed for hydraulic simulations and novel setting equations and penalties were included to limit the number of feasible solutions. The model was tested on two benchmark water networks, showing its capability to return near-optimal solutions within a short computational time.

关键词： Pump Scheduling meta-heuristic algorithm Harmony Search Number of switches Cost optimization

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A Self-Adaptive Improved Slime Mold algorithm for Multi-UAV Path Planning

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DRONES 2025年第3期9卷 219-219页

作者： Ma, Yuelin Zhang, Zeren Yao, Meng Fan, Guoliang Chinese Acad Sci Inst Automat Key Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

Multi-UAV path planning presents a critical challenge in Unmanned Aerial Vehicle (UAV) applications, particularly in environments with various obstacles and restrictions. These conditions transform multi-UAV path planning into a complex optimization problem with multiple constraints, significantly reducing the number of feasible solutions and complicating the generation of optimal flight trajectories. Although the slime mold algorithm (SMA) has proven effective in optimization missions, it still suffers from limitations such as inadequate exploration capacity, premature convergence, and a propensity to become stuck in local optima. These drawbacks degrade its performance in intricate multi-UAV scenarios. This study proposes a self-adaptive improved slime mold algorithm called AI-SMA to address these issues. Firstly, AI-SMA incorporates a novel search mechanism to balance exploration and exploitation by integrating ranking-based differential evolution (rank-DE). Then, a self-adaptive switch operator is introduced to increase population diversity in later iterations and avoid premature convergence. Finally, a self-adaptive perturbation strategy is implemented to provide an effective escape mechanism, facilitating faster convergence. Extensive experiments were conducted on the CEC 2017 benchmark test suite and multi-UAV path-planning scenarios. The results show that AI-SMA improves the quality of optimal fitness by approximately 7.83% over the original SMA while demonstrating superior robustness and effectiveness in generating collision-free trajectories.

关键词： multiple unmanned aerial vehicles path planning slime mold algorithm meta-heuristic algorithm

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Training Neuro-Fuzzy by Using meta-heuristic algorithms for MPPT

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Computer Systems Science & Engineering 2023年第4期45卷 69-84页

作者： Ceren Baştemur Kaya Ebubekir Kaya Göksel Gökkuş Department of Computer Technologies Nevsehir Vocational CollegeNevsehir Haci Bektas Veli UniversityNevşehir50300Turkey Department of Computer Engineering Faculty of Engineering and ArchitectureNevsehir Haci Bektas Veli UniversityNevşehir50300Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitectureNevsehir Haci Bektas Veli UniversityNevşehir50300Turkey

It is one of the topics that have been studied extensively on maximum power point tracking(MPPT)*** or soft computing methods are used for *** soft computing approaches are more effective than traditional approaches,studies on MPPT have shifted in this *** study aims comparison of performance of seven meta-heuristic training algorithms in the neuro-fuzzy training for *** meta-heuristic training algorithms used are particle swarm optimization(PSO),harmony search(HS),cuckoo search(CS),artificial bee colony(ABC)algorithm,bee algorithm(BA),differential evolution(DE)and flower pollination algorithm(FPA).The antecedent and conclusion parameters of neuro-fuzzy are determined by these *** data of a 250 W photovoltaic(PV)is used in the *** effective MPPT,different neuro-fuzzy structures,different membership functions and different control parameter values are evaluated in *** training algorithms are compared in terms of solution quality and convergence *** strengths and weaknesses of these algorithms are *** is seen that the type and number of membership function,colony size,number of generations affect the solution quality and convergence speed of the training *** a result,it has been observed that CS and ABC algorithm are more effective than other algorithms in terms of solution quality and convergence in solving the related problem.

关键词： Optimization meta-heuristic algorithm neuro-fuzzy MPPT photovoltaic system

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