DEMFFA: a multi-strategy modified Fennec Fox algorithm with mixed improved differential evolutionary variation strategies DOI Creative Commons
Gang Hu,

Keke Song,

Xiuxiu Li

et al.

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 8, 2024

Abstract The Fennec Fox algorithm (FFA) is a new meta-heuristic that primarily inspired by the fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. “No free lunch” theorem an has different effects in face of problems, such as: when solving high-dimensional or more complex applications, there are challenges as easily falling into local optimal slow convergence speed. To solve this problem FFA, paper, improved Fenna fox DEMFFA proposed adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, differential evolution mutation strategies. Firstly, mapping strategy added initialization stage make population distribution uniform, thus speeding up Secondly, order expedite speed algorithm, adjustments made factors whose position updated first stage, resulting faster convergence. Finally, prevent getting too early expand search space population, after second stages original update. In verify performance DEMFFA, qualitative analysis carried out on test sets, tested newly algorithms three sets. And we also CEC2020. addition, applied 10 practical engineering design problems 24-bar truss topology optimization problem, results show potential problems.

Language: Английский

Genghis Khan shark optimizer: A novel nature-inspired algorithm for engineering optimization DOI
Gang Hu,

Yuxuan Guo,

Guo Wei

et al.

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

Published: Oct. 1, 2023

Language: Английский

Citations

161
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm DOI Creative Commons
Mohammad Hussein Amiri, Nastaran Mehrabi Hashjin, Mohsen Montazeri

et al.

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

Citations

154
Crested Porcupine Optimizer: A new nature-inspired metaheuristic DOI
Mohamed Abdel‐Basset, Reda Mohamed, Mohamed Abouhawwash

et al.

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

Published: Dec. 22, 2023

Language: Английский

Citations

144
Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems DOI Creative Commons
Jun Wang, Wenchuan Wang,

Xiao-xue Hu

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(4)

Published: March 23, 2024

Abstract This paper innovatively proposes the Black Kite Algorithm (BKA), a meta-heuristic optimization algorithm inspired by migratory and predatory behavior of black kite. The BKA integrates Cauchy mutation strategy Leader to enhance global search capability convergence speed algorithm. novel combination achieves good balance between exploring solutions utilizing local information. Against standard test function sets CEC-2022 CEC-2017, as well other complex functions, attained best performance in 66.7, 72.4 77.8% cases, respectively. effectiveness is validated through detailed analysis statistical comparisons. Moreover, its application solving five practical engineering design problems demonstrates potential addressing constrained challenges real world indicates that it has significant competitive strength comparison with existing techniques. In summary, proven value advantages variety due excellent performance. source code publicly available at https://www.mathworks.com/matlabcentral/fileexchange/161401-black-winged-kite-algorithm-bka .

Language: Английский

Citations

108
Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization DOI
Wenchuan Wang,

Wei-can Tian,

Dong-mei Xu

et al.

Advances in Engineering Software, Journal Year: 2024, Volume and Issue: 195, P. 103694 - 103694

Published: June 15, 2024

Language: Английский

Citations

43
Optimal truss design with MOHO: A multi-objective optimization perspective DOI Creative Commons
Nikunj Mashru, Ghanshyam G. Tejani, Pinank Patel

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0308474 - e0308474

Published: Aug. 19, 2024

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The (HO) is novel meta-heuristic methodology draws inspiration from natural behaviour of hippos. HO built upon trinary-phase model incorporates mathematical representations crucial aspects Hippo's behaviour, including their movements aquatic environments, defense mechanisms against predators, and avoidance strategies. conceptual framework forms basis for developing multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions size constraints concerning stresses on individual sections constituent parts, these problems also involved competing objectives, such as reducing weight structure maximum nodal displacement. findings six popular methods were used compare results. Four industry-standard performance measures this comparison qualitative examination finest Pareto-front plots generated by each algorithm. average values obtained Friedman rank test analysis unequivocally showed MOHO outperformed other resolving significant quickly. In addition finding preserving more Pareto-optimal sets, recommended algorithm produced excellent convergence variance objective decision fields. demonstrated its potential navigating objectives through diversity analysis. Additionally, swarm effectively visualize MOHO's solution distribution across iterations, highlighting superior behaviour. Consequently, exhibits promise valuable method issues.

Language: Английский

Citations

36
Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization DOI
Mojtaba Ghasemi, Keyvan Golalipour, Mohsen Zare

et al.

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(15), P. 22913 - 23017

Published: July 1, 2024

Language: Английский

Citations

35
Novel hybrid kepler optimization algorithm for parameter estimation of photovoltaic modules DOI Creative Commons
Reda Mohamed, Mohamed Abdel‐Basset, Karam M. Sallam

et al.

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

Published: Feb. 11, 2024

Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this due their potential approximate the optimal solution for several complicated problems. Despite that, existing still suffer from sluggish convergence rates and stagnation in local optima when applied tackle problem. study presents new estimation technique, namely HKOA, based on integrating published Kepler algorithm (KOA) with ranking-based update exploitation improvement mechanisms estimate unknown parameters third-, single-, double-diode models. former mechanism aims at promoting KOA’s exploration operator diminish getting stuck optima, while latter strengthen its faster converge solution. Both KOA HKOA are validated using RTC France solar cell five PV modules, including Photowatt-PWP201, Ultra 85-P, STP6-120/36, STM6-40/36, show efficiency stability. In addition, they extensively compared techniques effectiveness. According experimental findings, strong alternative method estimating because it can yield substantially different superior findings

Language: Английский

Citations

22
Multi-objective Mantis Search Algorithm (MOMSA): A novel approach for engineering design problems and validation DOI
Mohammed Jameel, Mohamed Abouhawwash

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 422, P. 116840 - 116840

Published: Feb. 14, 2024

Language: Английский

Citations

18
Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Marie Hubálovská, Štěpán Hubálovský, Pavel Trojovský

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(3), P. 137 - 137

Published: Feb. 23, 2024

This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by operation mechanism. The algorithm is designed to address optimization problems, utilizing human-based approach. Taking cues from procedures, where defects are targeted and treated enhance beauty, BOA formulated mathematically modeled. Evaluation on CEC 2017 test suite showcases BOA’s ability balance exploration exploitation, delivering competitive solutions. Comparative analysis against twelve well-known algorithms demonstrates superior performance across various benchmark functions, with statistically significant advantages. Moreover, application constrained problems 2011 highlights effectiveness in real-world tasks.

Language: Английский

Citations

17