A new human-based metaheuristic algorithm for solving optimization problems on the base of simulation of driving training process

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: June 15, 2022

Abstract In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind DTBO design learning process to drive in school and training instructor. mathematically modeled three phases: (1) by instructor, (2) patterning students from instructor skills, (3) practice. performance evaluated on set 53 standard objective functions unimodal, high-dimensional multimodal, fixed-dimensional IEEE CEC2017 test types. results show that has been able provide appropriate solutions problems maintaining proper balance between exploration exploitation. quality compared with 11 well-known algorithms. simulation performs better competitor algorithms more efficient applications.

Language: Английский

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Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 58, P. 102210 - 102210

Published: Oct. 1, 2023

Language: Английский

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Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(4), P. 5235 - 5283

Published: Jan. 19, 2024

Language: Английский

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An enhanced black widow optimization algorithm for feature selection

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 235, P. 107638 - 107638

Published: Oct. 26, 2021

Language: Английский

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Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 29, 2024

Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. HO is conceived by drawing inspiration from inherent behaviors observed hippopotamuses, showcasing an innovative approach metaheuristic methodology. conceptually defined using trinary-phase model that incorporates their position updating rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained top rank 115 out 161 benchmark functions finding optimal value, encompassing unimodal high-dimensional multimodal functions, fixed-dimensional as well CEC 2019 test suite 2014 dimensions 10, 30, 50, 100 Zigzag Pattern suggests demonstrates noteworthy proficiency both exploitation exploration. Moreover, it effectively balances exploration exploitation, supporting search process. In light results addressing four distinct engineering design challenges, has achieved most efficient resolution while concurrently upholding adherence to designated constraints. performance evaluation algorithm encompasses various aspects, including comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, IWO recognized extensively researched metaheuristics, AOA recently developed algorithms, CMA-ES high-performance optimizers acknowledged for success IEEE competition. According statistical post hoc analysis, determined be significantly superior investigated algorithms. source codes publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .

Language: Английский

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DETDO: An adaptive hybrid dandelion optimizer for engineering optimization

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 57, P. 102004 - 102004

Published: June 8, 2023

Language: Английский

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Crested Porcupine Optimizer: A new nature-inspired metaheuristic

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 284, P. 111257 - 111257

Published: Dec. 22, 2023

Language: Английский

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An Effective Feature Selection Model Using Hybrid Metaheuristic Algorithms for IoT Intrusion Detection

Sensors, Journal Year: 2022, Volume and Issue: 22(4), P. 1396 - 1396

Published: Feb. 11, 2022

The increasing use of Internet Things (IoT) applications in various aspects our lives has created a huge amount data. IoT often require the presence many technologies such as cloud computing and fog computing, which have led to serious challenges security. As result these technologies, cyberattacks are also on rise because current security methods ineffective. Several artificial intelligence (AI)-based solutions been presented recent years, including intrusion detection systems (IDS). Feature selection (FS) approaches required for development intelligent analytic tools that need data pretreatment machine-learning algorithm-performance enhancement. By reducing number selected features, FS aims improve classification accuracy. This article presents new method through boosting performance Gorilla Troops Optimizer (GTO) based algorithm bird swarms (BSA). BSA is used boost exploitation GTO newly developed GTO-BSA it strong ability find feasible regions with optimal solutions. result, quality final output will increase, improving convergence. GTO-BSA's was evaluated using variety measures four IoT-IDS datasets: NSL-KDD, CICIDS-2017, UNSW-NB15 BoT-IoT. results were compared those original GTO, BSA, several state-of-the-art techniques literature. According findings experiments, had better convergence rate higher-quality

Language: Английский

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An improved whale optimization algorithm based on multi-population evolution for global optimization and engineering design problems

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 215, P. 119269 - 119269

Published: Nov. 17, 2022

Language: Английский

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Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 128, P. 107532 - 107532

Published: Dec. 12, 2023

Language: Английский

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