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In the context of complex structures and blurred cell boundaries present in breast cancer histopathological tissue images under a microscope, traditional thresholding methods struggle to accurately separate lesion areas in breast cancer image segmentation. To address this challenge, we propose a multi-threshold segmentation method for breast cancer images based on an improved Dandelion Optimization algorithm. This approach incorporates the concept of opposite-based learning and utilizes the improved Dandelion Optimization algorithm to calculate the maximum between-class variance as the optimization objective. Moreover, the method establishes fallback strategies and incorporates a memory matrix, while leveraging the golden jackal energy judgment mechanism to identify optimal thresholds. The experimental results show that compared with the Crow search algorithm, Harris Hawks optimization algorithm, artificial gorilla troop optimization algorithm, dandelion optimization algorithm, ocean predator algorithm, whale optimization algorithm, sparrow search algorithm, and sine cosine algorithm, and the improved Dandelion optimization algorithm achieves the highest fitness value and converges at the fastest speed when using the same threshold number, it also occupies an advantageous position in terms of peak signal-to-noise ratio, structural similarity index, feature similarity index, and mean square error. [ABSTRACT FROM AUTHOR]

To improve the tracking accuracy of moving targets in Non-Line-of-Sight (NLOS) environments and reduce positioning errors, a hybrid TDOA/AOA positioning method is proposed using the Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) joint localization. The method is based on an improved multi-objective dandelion optimization algorithm. In the optimization process of the fitness function for the positioning model using the dandelion optimization algorithm, a fast non-dominated sorting method is used to determine the core dandelions, and an approximation ideal solution sorting method is employed to achieve global optimization, thereby enhancing the speed and accuracy of the model solution. By introducing a multi-objective mechanism, the optimal individual of one population guides another population, accelerating the convergence speed, balancing the exploration and exploitation abilities to a certain extent, and enhancing the optimization performance. Simulation results show that compared to TDOA/AOA, Taylor, Chan, PSO, and DOA algorithms, the proposed algorithm outperforms TDOA/AOA algorithm and other similar algorithms. The algorithm exhibits excellent performance in terms of node positioning accuracy, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]

With the increasing demand for electrical energy and the challenges related to its production, along with the need to be environmentally friendly to achieve sustainability for future generations, proton exchange membrane fuel cells (PEMFCs) are emerging as a clean energy source that can effectively replace conventional energy sources, in various fields of application and especially in the field of transportation exploiting electric vehicles (EVs). To improve the development and control of the PEMFCs, the precise determination of its mathematical model remains an essential task. Indeed, the accuracy of such a model depends on the ability to overcome the constraints associated with the nonlinearity and the numerous involved unknown parameters. The present paper proposes a new Dandelion Optimizer (DO) to accurately identify, for the first time, the parameters of the PEMFC model. The DO addresses the weaknesses of the majority of metaheuristic algorithms related to the self-adaptation of parameters, the stagnation of convergence to local minima, and the ability to refer to the whole population. The high ability of the proposed method is investigated using both steady-state and dynamic situations. The DO-based parameters estimation approach has been assessed through a specific comparative study with the most recently published techniques including GWO, GBO, HHO, IAEO, VSDE, and ABCDESC is performed using two typical PEMFC modules, namely 250 W PEMFC and NedStack PS6. The results obtained proved that the proposed approach obtained promising achievements and better performances comparatively with well-recognized and competitive methods. [ABSTRACT FROM AUTHOR]