Boston Consulting Group Matrix-Based Equilibrium Optimizer for Numerical Optimization and Dynamic Economic Dispatch

Electronics, Год журнала: 2025, Номер 14(3), С. 456 - 456

Опубликована: Янв. 23, 2025

Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, system stability. With increasing demand diversifying structures, these grow increasingly complex. Metaheuristic algorithms have been highlighted their flexibility effectiveness addressing such complex problems. To further explore theoretical support metaheuristic this paper proposes a novel algorithm, Boston Consulting Group Matrix-based Equilibrium Optimizer (BCGEO), which integrates (EO) with classic economic decision-making model, Matrix. This matrix is utilized to construct model evaluating potential individuals, aiding rational allocation computational resources, thereby achieving better balance between exploration exploitation. In comparative experiments across various dimensions on CEC2017, BCGEO demonstrated superior search performance over its peers. Furthermore, dynamic dispatch, has shown strong capabilities Additionally, experimental results spacecraft trajectory problem suggest broader application fields.

Язык: Английский

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An active learning model based on image similarity for skin lesion segmentation

Neurocomputing, Год журнала: 2025, Номер 630, С. 129690 - 129690

Опубликована: Фев. 20, 2025

Язык: Английский

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An innovative segmentation algorithm based on enhanced fuzzy optimization of skin cancer images

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Язык: Английский

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Multilevel Thresholding Segmentation of Brain Tumor MRIs Using Type II Fuzzy Sets Based on an Improved Memory-Saving Heap-Based Optimizer

Опубликована: Янв. 1, 2025

Язык: Английский

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Improving the segmentation of digital images by using a modified Otsu’s between-class variance

Multimedia Tools and Applications, Год журнала: 2023, Номер 82(26), С. 40701 - 40743

Опубликована: Март 31, 2023

Язык: Английский

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How the choice of model calibration procedure affects projections of lake surface water temperatures for future climatic conditions

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133236 - 133236

Опубликована: Апрель 1, 2025

Язык: Английский

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Segmentation of brain MRI using moth-flame optimization with modified cross entropy based fitness function

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(32), С. 77945 - 77966

Опубликована: Фев. 24, 2024

Язык: Английский

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Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images

JUCS - Journal of Universal Computer Science, Год журнала: 2023, Номер 29(7), С. 759 - 804

Опубликована: Июль 24, 2023

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance categorization, some image segmentation techniques are presented extract regions of interest from CT images. Multi-level thresholding (MLT) one simplest most effective approaches, especially for grayscale images like scan Traditional methods use histogram approaches; however, these approaches encounter limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied resolve MLT, deemed an NP-hard optimization task. Despite advantages meta-heuristics solve global tasks, each approach has its own drawbacks. However, common flaw that they unable maintain diversity their population during search, which means might not always converge optimum. This study proposes cooperative intelligence-based MLT hybridizes parallel developing efficient method An model-based called CPGH developed based on three practical algorithms: particle (PSO), grey wolf optimizer (GWO), Harris hawks (HHO). In model, executed concurrently, number potential solutions moved across populations through procedure migration after set generations. model problem segmentation. proposed evaluated objective functions, cross-entropy, Otsu’s, Tsallis, over selected open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), universal quality (UQI) were employed quantify quality. overall ranking results indicate performance better than conventional PSO, GWO, HHO other state-of-the-art On tested images, offered average PSNR 24.8062, SSIM 0.8818, UQI 0.9097 20 thresholds.

Язык: Английский

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A velocity-guided Harris hawks optimizer for function optimization and fault diagnosis of wind turbine

Artificial Intelligence Review, Год журнала: 2022, Номер 56(3), С. 2563 - 2605

Опубликована: Июль 25, 2022

Язык: Английский

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Segmentation of 3D Anatomically Diffused Tissues in Magnetic Resonance Images Through Edge-Preserving Constrained Center-Free Fuzzy $C$-Means

IEEE Transactions on Fuzzy Systems, Год журнала: 2024, Номер 32(6), С. 3444 - 3457

Опубликована: Март 8, 2024

Anatomically diffused tissues (ADTs) refer to soft containing many anatomical regions that are spatially dispersed and structurally irregular. In magnetic resonance images, ADTs exhibit blurred morphology heterogeneous texture, making the accurate extraction of their 3D anatomy challenging. Center-free fuzzy C-means (FCM) can effectively partition nonlinear or nonspherical clusters, providing a promising scheme for ADT segmentation. It solves uncertainty arising from unreliable center estimation by introducing similarity criterion. However, criterion is sensitive number target objects adjacent members in images. Moreover, memberships existing algorithms susceptible losing real details. To handle these issues, we propose an edge-preserving constrained center-free FCM algorithm segmenting overcome sensitivity criterion, novel object-to-cluster measure first proposed utilize refined member-toobject adjacency. Specifically, focuses on feature space, which share approximately homogeneous characteristics with each object. Gradient-domain filtering then combined improved construct objective function FCM. With assistance designed imagedriven regularization, gradient information clusters constrained, eventually approaching guidance image. Experiments conducted two public brain datasets one local intrahepatic vein dataset. The results demonstrate more effective segmentation than state-of-the-art peers, exhibiting superior generalization capability.

Язык: Английский

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