Evolving Systems, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Evolving Systems, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Chaos Solitons & Fractals, Год журнала: 2024, Номер 185, С. 115111 - 115111
Опубликована: Июнь 15, 2024
Язык: Английский
Процитировано
7Computers in Biology and Medicine, Год журнала: 2024, Номер 180, С. 108984 - 108984
Опубликована: Авг. 10, 2024
Язык: Английский
Процитировано
6Alexandria Engineering Journal, Год журнала: 2024, Номер 93, С. 142 - 188
Опубликована: Март 15, 2024
In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO a math-inspired optimizer that has many limitations in handling complex multi-modal tries solve these drawbacks using 2 operators: phasor operator for diversity enhancement adaptive p-best mutation strategy preventing it converging local optima. To validate effectiveness suggested optimizer, comprehensive set comparative experiments CEC'2020 test suite was conducted. The experimental results consistently prove technique outperforms its counterparts terms both convergence speed accuracy. Moreover, algorithm applied multi-threshold method with Otsu's entropy, providing further evidence performance. evaluated by comparing those existing well-known algorithms at various threshold levels. proposed attains exceptional
Язык: Английский
Процитировано
5Neural Computing and Applications, Год журнала: 2025, Номер unknown
Опубликована: Янв. 12, 2025
Abstract To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial preventative healthcare. Timely identification significantly improves prognosis and treatment outcomes colorectal patients. Image segmentation in medical image analysis accurate planning. Therefore, this study, we present an alternative multilevel thresholding polyp method (MPOA) enhance of images. The proposed based on enhancing planet optimization algorithm (POA) by integrating operators from reptile search (RSA). evaluation developed MPOA tested with different images compared other approaches. results highlight superior capability MPOA, as evidenced various performance measures effectively segmenting Furthermore, metrics such peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), fitness values demonstrate that outperforms basic version POA methods. underscore significant impact RSA
Язык: Английский
Процитировано
0The Journal of Supercomputing, Год журнала: 2025, Номер 81(4)
Опубликована: Фев. 17, 2025
Язык: Английский
Процитировано
0Biomimetics, Год журнала: 2025, Номер 10(4), С. 218 - 218
Опубликована: Апрель 2, 2025
To better address the issue of multi-threshold image segmentation, this paper proposes a hybrid adaptive crayfish optimization algorithm with differential evolution for color segmentation (ACOADE). Due to insufficient convergence ability in later stages, it is challenging find more optimal solution optimization. ACOADE optimizes maximum foraging quantity parameter p and introduces an adjustment strategy enhance randomness algorithm. Furthermore, core formula (DE) incorporated balance ACOADE’s exploration exploitation capabilities better. validate performance ACOADE, IEEE CEC2020 test function was selected experimentation, eight other algorithms were chosen comparison. verify effectiveness threshold Kapur entropy method Otsu used as objective functions compared algorithms. Subsequently, peak signal-to-noise ratio (PSNR), feature similarity index measure (FSIM), structural (SSIM), Wilcoxon employed evaluate quality segmented images. The results indicated that exhibited significant advantages terms value, metrics, convergence, robustness.
Язык: Английский
Процитировано
0Learning and analytics in intelligent systems, Год журнала: 2025, Номер unknown, С. 401 - 412
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Journal of Energy Storage, Год журнала: 2025, Номер 120, С. 116381 - 116381
Опубликована: Апрель 9, 2025
Язык: Английский
Процитировано
0Computers in Biology and Medicine, Год журнала: 2024, Номер 182, С. 109175 - 109175
Опубликована: Сен. 24, 2024
Язык: Английский
Процитировано
3IEEE Access, Год журнала: 2024, Номер 12, С. 148155 - 148166
Опубликована: Янв. 1, 2024
To address the problems of slow convergence, low search accuracy, and easy fall into local optimum, generating a large number infeasible solutions when solving 0-1 Knapsack Problem, which makes it difficult to obtain optimal solution scheme, in this paper, we present greedy induced mutation method for locally solutions, namely, an improved Shuffled Frog Leaping Algorithm (Shuffled based on Greedy Mutation, SFLA-GA-M). First, proposed algorithm adjusts population generation individuals SFLA avoid thereby optimizing update strategy. Secondly, mechanism that incorporates both algorithms genetic is introduced enhance accuracy algorithm. Finally, ten classical Problem cases were selected prove feasibility robustness SFLA-GA-M by comparing with other two variants, are MDSFLA, DSFLA. Meanwhile, further verify performance SFLA-GA-M, KPs multi-dimensional test compared five algorithm, including: DEA, PSO, GA, BMA IFMA. The experimental results show has achieved stronger convergence stability displayed better efficiency superior global capability than IFMA large-sized Problem.
Язык: Английский
Процитировано
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