Bald eagle search algorithm: a comprehensive review with its variants and applications

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

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Network Traffic Prediction Based on Decomposition and Combination Model

International Journal of Communication Systems, Год журнала: 2025, Номер 38(6)

Опубликована: Март 8, 2025

ABSTRACT In this paper, a combination model based on complementary ensemble empirical mode decomposition (CEEMD) is proposed. First, CEEMD applied to decompose original network traffic generate high‐frequency component, low‐frequency and residual component. Then, the components are modeled predicted using bi‐directional long short‐term memory (BiLSTM). The component autoregressive integrated moving average (ARIMA). Meanwhile, considering that BiLSTM influenced by hyperparameters, an Improved Bald Eagle Search (IBES) algorithm proposed optimize three hyperparameters of BiLSTM, avoiding blindness subjectivity manual selection parameters. Finally, prediction values ARIMA summed obtain final value traffic. comparisons with other models proved closer real data, optimal performance indicators, which very suitable for high precision occasions.

Язык: Английский

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A hybrid butterfly and Newton–Raphson swarm intelligence algorithm based on opposition-based learning

Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14469 - 14514

Опубликована: Июль 21, 2024

Язык: Английский

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