Crested Porcupine Optimizer: A new nature-inspired metaheuristic

Knowledge-Based Systems, Год журнала: 2023, Номер 284, С. 111257 - 111257

Опубликована: Дек. 22, 2023

Язык: Английский

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Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problems

Artificial Intelligence Review, Год журнала: 2024, Номер 57(4)

Опубликована: Март 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 .

Язык: Английский

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A Sinh Cosh optimizer

Knowledge-Based Systems, Год журнала: 2023, Номер 282, С. 111081 - 111081

Опубликована: Окт. 18, 2023

Язык: Английский

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Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 415, С. 116200 - 116200

Опубликована: Июль 10, 2023

Язык: Английский

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Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience

Biomimetics, Год журнала: 2023, Номер 8(5), С. 386 - 386

Опубликована: Авг. 24, 2023

In this research article, we uphold the principles of No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration exploitation, drawing inspiration from strategic dynamics player conduct observed in sport golf. Through comprehensive assessments encompassing fifty-two objective functions four real-world engineering applications, efficacy rigorously examined. results optimization process reveal GOA’s exceptional proficiency both exploitation strategies, effectively striking harmonious equilibrium between two. Comparative analyses against ten competing algorithms demonstrate clear statistically significant superiority across spectrum performance metrics. Furthermore, successful application intricate energy commitment problem, considering network resilience, underscores its prowess addressing complex challenges. For convenience community, provide MATLAB implementation codes for proposed methodology, ensuring accessibility facilitating further exploration.

Язык: Английский

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A novel metaheuristic inspired by horned lizard defense tactics

Artificial Intelligence Review, Год журнала: 2024, Номер 57(3)

Опубликована: Фев. 16, 2024

Abstract This paper introduces HLOA, a novel metaheuristic optimization algorithm that mathematically mimics crypsis, skin darkening or lightening, blood-squirting, and move-to-escape defense methods. In crypsis behavior, the lizard changes its color by becoming translucent to avoid detection predators. The horned can lighten darken skin, depending on whether not it needs decrease increase solar thermal gain. lightening strategy is modeled including stimulating hormone melanophore rate( $$\alpha$$ α -MHS) influences these changes. Further, move-to-evasion also described. lizard’s shooting blood mechanism, described as projectile motion, modeled. These strategies balance exploitation exploration mechanisms for local global search over solution space. HLOA performance benchmarked with sixty-three problems from literature, testbench provided in IEEE CEC- 2017 “Constrained Real-Parameter Optimization”, analyzed dimensions 10, 30, 50, 100, well functions CEC-06 2019 “100-Digit Challenge”. Moreover, three real-world constraint applications CEC2020 two engineering problems, multiple gravity assist optimal power flow problem, are studied. Wilcoxon Friedman statistics tests compare results against ten recent bio-inspired algorithms. shows provides most more effectively than competing At same time, test ranks first, n-dimensional analysis performs better constrained 50 100. source code free available https://www.mathworks.com/matlabcentral/fileexchange/159658-horned-lizard-optimization-algorithm-hloa .

Язык: Английский

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Arctic puffin optimization: A bio-inspired metaheuristic algorithm for solving engineering design optimization

Advances in Engineering Software, Год журнала: 2024, Номер 195, С. 103694 - 103694

Опубликована: Июнь 15, 2024

Язык: Английский

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Particle guided metaheuristic algorithm for global optimization and feature selection problems

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123362 - 123362

Опубликована: Фев. 1, 2024

Язык: Английский

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Optimal truss design with MOHO: A multi-objective optimization perspective

PLoS ONE, Год журнала: 2024, Номер 19(8), С. e0308474 - e0308474

Опубликована: Авг. 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.

Язык: Английский

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Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization

The Journal of Supercomputing, Год журнала: 2024, Номер 80(15), С. 22913 - 23017

Опубликована: Июль 1, 2024

Язык: Английский

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