Computer Science Review, Год журнала: 2024, Номер 53, С. 100647 - 100647
Опубликована: Июнь 7, 2024
Язык: Английский
Knowledge-Based Systems, Год журнала: 2023, Номер 268, С. 110454 - 110454
Опубликована: Март 11, 2023
Язык: Английский
Процитировано
292
Scientific Reports, Год журнала: 2023, Номер 13(1)
Опубликована: Май 31, 2023
This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed WaOA design are the process of feeding, migrating, escaping, and fighting predators. implementation steps mathematically modeled three phases exploration, migration, exploitation. Sixty-eight standard benchmark functions consisting unimodal, high-dimensional multimodal, fixed-dimensional CEC 2015 test suite, 2017 suite to evaluate performance optimization applications. results unimodal indicate exploitation ability WaOA, multimodal exploration suites high balancing during search process. is compared with ten well-known algorithms. simulations demonstrate that due its excellent balance exploitation, capacity deliver superior for most functions, has exhibited remarkably competitive contrast other comparable In addition, use addressing four engineering issues twenty-two real-world problems from 2011 demonstrates apparent effectiveness MATLAB codes available https://uk.mathworks.com/matlabcentral/profile/authors/13903104 .
Язык: Английский
Процитировано
116
Journal of Bionic Engineering, Год журнала: 2023, Номер 20(4), С. 1747 - 1765
Опубликована: Март 1, 2023
Язык: Английский
Процитировано
59
Scientific Reports, Год журнала: 2023, Номер 13(1)
Опубликована: Авг. 9, 2023
This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk (RTH). As predator, has hunting strategy from detecting prey until swoop stage. There are three stages during process. In high soaring stage, explores search space and determines area with location. low moves inside selected around to choose best position for hunt. Then, swings hits its target in stooping swooping stages. The proposed mimics prey-hunting method of solving real-world problems. performance RTH been evaluated on classes first class includes specific kinds problems: 22 standard benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal IEEE Congress Evolutionary Computation 2020 (CEC2020), CEC2022. is compared eight recent algorithms confirm contribution these considered Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle (MGO), Gorilla Troops (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila (AO), Harris Hawks (HHO). results regarding accuracy, robustness, convergence speed. second seven engineering problems that will be investigate other published profoundly. Finally, proton exchange membrane fuel cell (PEMFC) extraction parameters performed evaluate complex problem. several papers approve performance. ultimate each ability provide higher most cases. For class, mostly got optimal solutions functions faster provided better third when resolving real word or extracting PEMFC parameters.
Язык: Английский
Процитировано
53
Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 122, С. 106121 - 106121
Опубликована: Март 15, 2023
Язык: Английский
Процитировано
51
IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2024, Номер 25(9), С. 12517 - 12527
Опубликована: Март 19, 2024
In this paper, a large-scale multi-objective gate assignment model is constructed by considering the flight international and domestic attributes, task type, airline affiliation, aircraft type. Then quantum-inspired evolutionary algorithm based on decomposition mechanism, namely MOQEA/D developed to solve effectively. Specifically, new mechanism designed decompose GAP into several single-objective sub-GAPs. Each quantum bit string solves sub-GAP independently. And optimal crossover strategy proposed limit randomness of observation operations maximize preservation excellent genes further improve optimization performance. Finally, knapsack problem are selected verify effectiveness MOQEA/D. The experiment results demonstrate that can effectively obtain ideal results. It takes very significance application value in solving complex problems.
Язык: Английский
Процитировано
42
Journal of Bionic Engineering, Год журнала: 2024, Номер 21(2), С. 1092 - 1115
Опубликована: Фев. 28, 2024
Язык: Английский
Процитировано
14
Alexandria Engineering Journal, Год журнала: 2024, Номер 87, С. 543 - 573
Опубликована: Янв. 1, 2024
The butterfly optimization algorithm (BOA) is a meta-heuristic that mimics foraging and mating behavior of butterflies. In order to alleviate the problems slow convergence, local optimum lack population diversity BOA, an enhanced adaptive (EABOA) proposed in this paper. First, new fragrance model designed, which provided finer perception way effectively convergence speed accuracy. Second, Lévy flight with high-frequency short-step jumping low-frequency long-step walking adopted help jump out optimum. Third, dimension learning-based hunting employed enhance information exchange by creating neighbors for each butterfly, thus improving balance between global search maintaining diversity. addition, Fitness-Distance-Constraint (FDC) method introduced constraint handling EABOA (named FDC-EABOA). compared 8 well-known algorithms BOA variants CEC 2022 test suite results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed Quade rank multiple comparisons, analysis variance (ANOVA) range analysis. Finally, FDC-EABOA are applied seven engineering (parameter identification photovoltaic module model, reducer design, tension/compression spring pressure vessel gear train welded beam SOPWM 3-level inverters), metrics such as Improvement Index (IF) Mean Constraint Violation (MV) confirm satisfactory. Experimental statistical show outperform comparison demonstrate strong potential solving numerical design problems.
Язык: Английский
Процитировано
12
Systems Science & Control Engineering, Год журнала: 2024, Номер 12(1)
Опубликована: Авг. 1, 2024
Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global local searches during optimization, BES effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers comprehensive review research on BES. Beginning with an introduction BES's natural inspiration conceptual framework, it explores modifications, hybridizations, applications domains. Then, critical evaluation performance provided, offering update effectiveness compared recently published algorithms. Furthermore, presents meta-analysis developments outlines potential future directions. As swarm-inspired algorithms become increasingly important tackling complex problems, this study valuable resource for researchers aiming understand algorithms, mainly focusing comprehensively. It investigates evolution, exploring solving intricate fields.
Язык: Английский
Процитировано
12
IEEE Access, Год журнала: 2024, Номер 12, С. 26062 - 26095
Опубликована: Янв. 1, 2024
The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of gas in liquid under specific pressure conditions. Since its introduction Hashim et al. 2019, HGSO has gained significant attention for unique features, including minimal adaptive parameters and balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey HGSO, covering walk through historical development modifications, hybridizations with other algorithms, showcasing adaptability potential synergy. Recent variants are categorized into modified, hybridized, multi-objective versions, review explores main applications, demonstrating effectiveness solving complex problems. evaluation includes discussion algorithm's strengths weaknesses. comprehensive review, featuring graphical tabular comparisons, not only indicates future directions field but also serves as valuable resource researchers seeking deep understanding advanced versions. As algorithms gain prominence intricate optimization problems, this insights applications across diverse domains.
Язык: Английский
Процитировано
11