Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109370 - 109370
Опубликована: Окт. 23, 2024
Язык: Английский
Advanced Engineering Informatics, Год журнала: 2024, Номер 61, С. 102464 - 102464
Опубликована: Март 15, 2024
Язык: Английский
Процитировано
31
Applied Soft Computing, Год журнала: 2024, Номер 165, С. 112108 - 112108
Опубликована: Авг. 23, 2024
Язык: Английский
Процитировано
7
Journal Of Big Data, Год журнала: 2024, Номер 11(1)
Опубликована: Июнь 18, 2024
Abstract The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. algorithm employs a soft-RIME search strategy hard-RIME puncture mechanism, along with improved positive greedy resist getting trapped in local optima enhance its overall capabilities. also Binary modified (mRIME), binary adaptation of address unique challenges posed FS problems, which typically involve spaces. Four different types transfer functions (TFs) were selected for issues, their efficacy was investigated CEC2011 CEC2017 tasks related disease diagnosis. results proposed mRIME tested on ten reliable algorithms. advanced architecture demonstrated superior performance tasks, providing effective solution complex problems various domains.
Язык: Английский
Процитировано
6
Journal of Computational Design and Engineering, Год журнала: 2023, Номер 10(6), С. 2223 - 2250
Опубликована: Окт. 26, 2023
Abstract The coati optimization algorithm (COA) is a meta-heuristic proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In group organization coatis, half coatis climb trees chase their prey away, while other wait beneath catch it (ii) Coatis avoidance predators behavior, which gives strong global exploration ability. However, over course our experiment, we uncovered opportunities for enhancing algorithm’s performance. When confronted with intricate problems, certain limitations surfaced. Much like long-nosed raccoon gradually narrowing its search range as approaches optimal solution, COA exhibited tendencies that could result reduced convergence speed risk becoming trapped local optima. this paper, propose an improved (ICOA) enhance efficiency. Through sound-based envelopment strategy, can capture more quickly accurately, allowing converge rapidly. By employing physical exertion have greater variety escape options when being chased, thereby exploratory capabilities ability Finally, lens opposition-based learning strategy added improve To validate performance ICOA, conducted tests using IEEE CEC2014 CEC2017 benchmark functions, well six engineering problems.
Язык: Английский
Процитировано
13
Biomimetics, Год журнала: 2024, Номер 9(6), С. 361 - 361
Опубликована: Июнь 14, 2024
The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic that based on crayfish’s summer escape behavior, competitive and foraging behavior. COA has good performance, but it still suffers from the problems of slow convergence speed sensitivity to local optimum. To solve these problems, an improved multi-strategy for solving numerical called IMCOA, address shortcomings original each behavioral strategy. Aiming at imbalance between exploitation global exploration heat avoidance competition phases, this paper proposes cave candidacy strategy fitness–distance balanced strategy, respectively, so two behaviors can better coordinate capabilities falling into optimum prematurely. directly formula modified during phase. food covariance learning utilized enhance population diversity improve accuracy speed. Finally, introduction optimal non-monopoly search perturb solution updates improves algorithm’s ability obtain best solution. We evaluated effectiveness IMCOA using CEC2017 CEC2022 test suites compared with eight algorithms. Experiments were conducted different dimensions by performing analyses, stability Wilcoxon rank–sum tests Friedman tests. show strike balance outperforms traditional other algorithms terms its speed, accuracy, avoid premature convergence. Statistical analysis shows there significant difference performance Additionally, three engineering design confirm practicality potential real-world problems.
Язык: Английский
Процитировано
5
Computers in Biology and Medicine, Год журнала: 2024, Номер 183, С. 109272 - 109272
Опубликована: Окт. 16, 2024
Язык: Английский
Процитировано
4
Applied Intelligence, Год журнала: 2025, Номер 55(4)
Опубликована: Янв. 11, 2025
Язык: Английский
Процитировано
0
Biomimetics, 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
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Апрель 13, 2025
Язык: Английский
Процитировано
0
Multimedia Tools and Applications, Год журнала: 2023, Номер 83(14), С. 41051 - 41081
Опубликована: Окт. 11, 2023
Язык: Английский
Процитировано
10