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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Fast random opposition-based learning Golden Jackal Optimization algorithm

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 275, P. 110679 - 110679

Published: June 5, 2023

Language: Английский

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Enhanced opposition-based grey wolf optimizer for global optimization and engineering design problems

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 76, P. 429 - 467

Published: June 22, 2023

A recently developed swarm-based meta-heuristic algorithm namely Grey Wolf Optimization (GWO), which is based on the hunting and leadership behaviours of grey wolves in nature, has shown superior performance when compared with existing algorithms. However, like other approaches, GWO limitation poor exploitation ability being stuck local optima solving challenging optimization problems. To overcome these limitations, a novel technique, "Enhanced Opposition-Based Learning" (EOBL), been proposed implemented algorithm. The EOBL technique largely inspired by Learning (OBL) Random (ROBL) techniques to efficiently balance exploration exploitation. As result, Enhanced Optimizer (EOBGWO), an innovative approach, increase effectiveness conventional test efficiency EOBGWO method, it tested standard IEEECEC2005, IEEECEC2017, IEEECEC2019 functions, along several real-life engineering design Furthermore, evaluate stability evaluated IEEECEC2008 special session large scale global experimental outcomes statistical measures such as t-test Wilcoxon rank-sum demonstrate that method outperforms state-of-the-art algorithms both

Language: Английский

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A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107212 - 107212

Published: July 6, 2023

Language: Английский

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An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(2), P. 1092 - 1115

Published: Feb. 28, 2024

Language: Английский

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Fast random opposition-based learning Aquila optimization algorithm

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26187 - e26187

Published: Feb. 1, 2024

Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been continuous effort develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is newly established swarm-based method that mimics the hunting strategy birds in nature. However, complex problems, AO shown sluggish convergence rate gets stuck local optimal region throughout process. To overcome this problem, study, mechanism named Fast Random Opposition-Based Learning (FROBL) combined with improve proposed approach called FROBLAO algorithm. validate performance algorithm, CEC 2005, 2019, 2020 test functions, along six real-life engineering tested. Moreover, statistical analyses such as Wilcoxon rank-sum test, t-test, Friedman performed analyze significant difference between other results demonstrate achieved outstanding effectiveness solving an extensive

Language: Английский

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Chaotic-Based Mountain Gazelle Optimizer for Solving Optimization Problems

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: May 6, 2024

Abstract The Mountain Gazelle Optimizer (MGO) algorithm has become one of the most prominent swarm-inspired meta-heuristic algorithms because its outstanding rapid convergence and excellent accuracy. However, MGO still faces premature convergence, making it challenging to leave local optima if early-best solutions neglect relevant search domain. Therefore, in this study, a newly developed Chaotic-based (CMGO) is proposed with numerous chaotic maps overcome above-mentioned flaws. Moreover, ten distinct were simultaneously incorporated into determine optimal values enhance exploitation promising solutions. performance CMGO been evaluated using CEC2005 CEC2019 benchmark functions, along four engineering problems. Statistical tests like t-test Wilcoxon rank-sum test provide further evidence that outperforms existing eminent algorithms. Hence, experimental outcomes demonstrate produces successful auspicious results.

Language: Английский

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Improved honey badger algorithm based on elementary function density factors and mathematical spirals in polar coordinate systema

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(3)

Published: Feb. 15, 2024

Abstract The Honey Badger Algorithm (HBA) is a new swarm intelligence optimization algorithm by simulating the foraging behavior of honey badgers in nature. To further improve its convergence speed and accuracy, an improved HBA based on density factors with elementary functions mathematical spirals polar coordinate system was proposed. proposes six for attenuation states functions, introduces expressions diameters angles seven (Fibonacci spiral, Butterfly curve, Rose Cycloid, Archimedean Hypotrochoid Cardioid) best synthesized effect to replace strategy badger digging pattern HBA. By using 23 benchmark test above improvements are sequentially compared original HBA, improvement, α4CycρHBA, selected be SOA, MVO, DOA, CDO, MFO, SCA, BA, GWO FFA. Finally, four engineering design problems (pressure vessel design, three-bar truss cantilever beam slotted bulkhead design) were solved. simulation experiments results show that proposed has characteristics balanced exploration expiration, fast high able solve function better way.

Language: Английский

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An Improved Golden Jackal Optimization Algorithm Using Opposition-Based Learning for Global Optimization and Engineering Problems

International Journal of Computational Intelligence Systems, Journal Year: 2023, Volume and Issue: 16(1)

Published: Sept. 12, 2023

Abstract Golden Jackal Optimization (GJO) is a recently developed nature-inspired algorithm that motivated by the collaborative hunting behaviours of golden jackals in nature. However, GJO has disadvantage poor exploitation ability and easy to get stuck an optimal local region. To overcome these disadvantages, this paper, enhanced variant jackal optimization incorporates opposition-based learning (OBL) technique (OGJO) proposed. The OBL implemented into with probability rate, which can assist escaping from optima. validate efficiency OGJO, several experiments have been performed. experimental outcomes revealed proposed OGJO more than other compared algorithms.

Language: Английский

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A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(3), P. 1749 - 1822

Published: Dec. 11, 2023

Abstract Optimization is a method which used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also our daily activities. In everyone always wants to minimize or maximize something called the objective function. Traditional modern optimization techniques Meta-Heuristic (MH) are solve functions. But traditional fail complex real-world problem consisting of non-linear So many have been proposed exponentially over last few decades overcome these challenges. This paper discusses brief review different benchmark test functions (BTFs) related existing MH algorithms (OA). It classification reported literature regarding swarm-based, human-based, physics-based, evolutionary-based methods. Based on half-century literature, MH-OAs tabulated terms year, author, inspiration agent. Furthermore, this presents MATLAB python code web-link MH-OA. After reading article, readers will be able use MH-OA challenges their field.

Language: Английский

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