Orthogonal opposition-based learning honey badger algorithm with differential evolution for global optimization and engineering design problems

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 91, P. 348 - 367

Published: Feb. 19, 2024

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity an imbalance between exploitation. In this paper, improved honey called ODEHBA proposed to improve the performance of basic HBA. Firstly, orthogonal opposition-based learning technique employed assist population escaping local optimum. Secondly, differential evolution utilized ensure enrichment diversity enhance convergence speed. Finally, capability boosted by equilibrium pool strategy. To validate efficacy ODEHBA, compared with 13 well-known algorithms on CEC2022 benchmark test sets. Friedman Wilcoxon rank-sum are assess ODEHBA. Furthermore, three engineering design problems Internet Vehicles (IoV) routing problem applied The simulation results demonstrate solving complex numerical problems, design, IoV problems. This holds significant practical implications for cost reduction resource utilization.

Language: Английский

---

Evaluation and Interpretation of Blasting-Induced Tunnel Overbreak: Using Heuristic-Based Ensemble Learning and Gene Expression Programming Techniques

Rock Mechanics and Rock Engineering, Journal Year: 2024, Volume and Issue: 57(9), P. 7535 - 7563

Published: May 14, 2024

Language: Английский

---

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(3)

Published: Feb. 2, 2024

Language: Английский

---

River Water Temperature Prediction Using a Hybrid Model Based on Variational Mode Decomposition (VMD) and Outlier Robust Extreme Learning Machine

Environmental Processes, Journal Year: 2024, Volume and Issue: 11(3)

Published: July 19, 2024

Language: Английский

---

Daily prediction of Urmia Lake water level using remote sensing data and honey badger optimization-based data-driven models

Acta Geophysica, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

---

Designing Empirical Fourier Decomposition Reinforced with Multiscale Increment Entropy and Deep Learning to Forecast Dry Bulb Air Temperature

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104597 - 104597

Published: March 1, 2025

Language: Английский

---

Enhancement groundwater level prediction using hybrid ANN-HHO model: case study (Shabestar Plain in Iran)

Arabian Journal of Geosciences, Journal Year: 2023, Volume and Issue: 16(8)

Published: July 10, 2023

Language: Английский

---

Forecasting of lake water level based on a hybrid model of innovative gunner algorithm

Acta Geophysica, Journal Year: 2023, Volume and Issue: 72(4), P. 2695 - 2713

Published: Sept. 7, 2023

Language: Английский

---

A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Franklin Open, Journal Year: 2024, Volume and Issue: 8, P. 100141 - 100141

Published: Aug. 10, 2024

Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems various fields. These have become popular because of their ability to explore exploit solutions problem domains. Honey Badger Algorithm (HBA) is a population-based metaheuristic algorithm inspired by the dynamic hunting strategy honey badgers, utilizing digging-seeking techniques. Since its introduction 2020, HBA has garnered widespread attention been applied across This review aims comprehensively survey improvement application problems. Additionally, conducts meta-analysis HBA's improvements, hybridization since introduction. According result survey, 52 studies presented improved using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism opposition based learning techniques, 20 hybrid with other metaheuristics 101 uses original for wide acceptance within research community stems from straightforwardness, ease use, efficient computational time, accelerated convergence speed, high efficacy, capability address different kind issues, distinguishing it well-known approches presented.

Language: Английский

---

Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization

Water Science & Technology Water Supply, Journal Year: 2024, Volume and Issue: 24(3), P. 847 - 864

Published: Feb. 26, 2024

Abstract Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of production system examining four pivotal factors: buried pipe length (TL), air flow rate (AFR), temperature (AT), (AH). A positive correlation between these variables vapor established, with AT exerting most pronounced influence. Significantly, analysis variance reveals main interactive effects variables, except for TL × AFR, at 5% significance level. To enhance understanding intricate interplay among factors, proficient least squares support vector machines model devised, employing radial basis function kernel. demonstrates an impressive 98% concurrence projected empirical data, minimal error 0.66 mL 5.99%. An in-depth sensitivity underscores differential impact AT, AH, TL, AFR on (WV) prediction. Optimal values 3.98 m, 6.89 m3/h, 46.30 °C, 86.62% respectively, are obtained through subsequent optimization independent using genetic algorithms, resulting in notable 23.61 mL.

Language: Английский

---