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Red-tailed hawk algorithm for numerical optimization and real-world problems
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OA
AI
DOI：10.1038/s41598-023-38778-3.png)
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摘要
En 中文
This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.
Keyword:
PARTICLE SWARM OPTIMIZATION
WATER CYCLE ALGORITHM
FUEL-CELL
DIFFERENTIAL EVOLUTION
PARAMETER EXTRACTION
GEOGRAPHIC-VARIATION
FEATURE-SELECTION
SEARCH ALGORITHM
IDENTIFICATION
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期刊
IF:
3.9
论文数:
27.9W
被引数:
83.5W
机构
引用论文
A novel hybrid arithmetic optimization algorithm for solving constrained optimization problems一种求解约束优化问题的混合算术优化算法
PREAI
分享收藏分享收藏A Novel Meta-Heuristic Algorithm for Numerical and Engineering Optimization Problems: Piranha Foraging Optimization Algorithm (PFOA).png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
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