Secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(5)

Published: April 23, 2024

Abstract This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization (SBOA), inspired by the survival behavior of birds in their natural environment. Survival for involves continuous hunting prey and evading pursuit from predators. information is crucial proposing new that utilizes abilities to address real-world problems. The algorithm's exploration phase simulates snakes, while exploitation models escape During this phase, observe environment choose most suitable way reach secure refuge. These two phases are iteratively repeated, subject termination criteria, find optimal solution problem. To validate performance SBOA, experiments were conducted assess convergence speed, behavior, other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using CEC-2017 CEC-2022 benchmark suites. All test results consistently demonstrated outstanding terms quality, stability. Lastly, was employed tackle 12 constrained engineering design problems perform three-dimensional path planning Unmanned Aerial Vehicles. demonstrate that, contrasted optimizers, proposed can better solutions at faster pace, showcasing its significant potential addressing

Language: Английский

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

Biomimetics, Journal Year: 2023, Volume and Issue: 8(5), P. 386 - 386

Published: Aug. 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.

Language: Английский

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

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

Published: March 5, 2024

Abstract This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve optimization capabilities of conventional optimizer in order address problem data clustering. The process that groups similar items within dataset into non-overlapping groups. Grey hunting behaviour served as model for however, it frequently lacks exploration and exploitation are essential efficient work mainly focuses on enhancing using weight factor concepts increase variety avoid premature convergence. Using partitional clustering-inspired fitness function, was extensively evaluated ten numerical functions multiple real-world datasets with varying levels complexity dimensionality. methodology is based incorporating concept purpose refining initial solutions adding diversity during phase. results show performs much better than standard discovering optimal clustering solutions, indicating higher capacity effective solution space. found able produce high-quality cluster centres fewer iterations, demonstrating its efficacy efficiency various datasets. Finally, demonstrates robustness dependability resolving issues, which represents significant advancement over techniques. In addition addressing shortcomings algorithm, incorporation innovative establishes further metaheuristic algorithms. performance around 34% original both test problems problems.

Language: Английский

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Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

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

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Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 239 - 239

Published: June 6, 2023

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions The main inspiration for DA simulate selection of objects from different drawers create optimal combination. process involves dresser with given number drawers, where similar items are placed each drawer. based on selecting suitable items, discarding unsuitable ones and assembling them into appropriate described, its mathematical modeling presented. performance tested by solving fifty-two objective functions various unimodal multimodal types CEC 2017 test suite. results compared twelve well-known algorithms. simulation demonstrate that DA, proper balance between exploration exploitation, produces solutions. Furthermore, comparing shows effective problems much more competitive than against which it was to. Additionally, implementation twenty-two constrained 2011 suite demonstrates high efficiency handling real-world applications.

Language: Английский

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OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 468 - 468

Published: Oct. 1, 2023

This study proposes the One-to-One-Based Optimizer (OOBO), a new optimization technique for solving problems in various scientific areas. The key idea designing suggested OOBO is to effectively use knowledge of all members process updating algorithm population while preventing from relying on specific population. We one-to-one correspondence between two sets and selected as guides increase involvement update process. Each member chosen just once guide only utilized another this interaction. proposed OOBO's performance evaluated with fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal types, CEC 2017 test suite. results highlight remarkable capacity strike balance exploration exploitation within problem-solving space during search quality achieved using by comparing them eight well-known algorithms. simulation findings show that outperforms other algorithms addressing can give more acceptable quasi-optimal solutions. Also, implementation six engineering shows effectiveness approach real-world applications.

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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Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 470 - 470

Published: Oct. 1, 2023

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates natural behavior of kookaburras in nature. The fundamental inspiration KOA strategy when hunting and killing prey. theory stated, its mathematical modeling presented following two phases: (i) exploration based on simulation prey (ii) exploitation kookaburras’ ensuring that their killed. performance has been evaluated 29 standard benchmark functions from CEC 2017 test suite for different problem dimensions 10, 30, 50, 100. optimization results show proposed approach, by establishing balance between exploitation, good efficiency managing effective search process providing suitable solutions problems. obtained using have compared with 12 well-known algorithms. analysis shows KOA, better most functions, provided superior competition addition, implementation 22 constrained problems 2011 suite, as well 4 engineering design problems, approach acceptable to competitor algorithms handling real-world applications.

Language: Английский

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Integrated thermal error modeling and compensation of machine tool feed system using subtraction-average-based optimizer-based CNN-GRU neural network

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 131(12), P. 6075 - 6089

Published: March 6, 2024

Language: Английский

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Enhancing Rolling Bearing Fault Diagnosis in Motors using the OCSSA-VMD-CNN-BiLSTM Model: A Novel Approach for Fast and Accurate Identification

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 78463 - 78479

Published: Jan. 1, 2024

This study addresses the challenges posed by strong noise and nonstationary characteristics of vibration signals to enhance efficiency accuracy rolling-bearing fault diagnosis in electric motors. A model is proposed based on improved variational mode decomposition (VMD) a convolutional neural network bidirectional long short-term memory (CNN-BiLSTM). In feature extraction stage, Osprey-Cauchy-Sparrow search algorithm (OCSSA) optimizes modal number K penalty coefficient α VMD, facilitating reconstruction original extract features minimum envelope entropy criterion. mean, variance, peak value, kurtosis, RMS peak-to-average ratio (PAR), impulse factors, form factor, clearance factor were computed from reconstructed signals. These indicators used construct vector for each sample, serving as input OCSSA-VMD-CNN-BiLSTM model, which quickly accurately identifies types. Experimental verification confirms that this method enhances speed identification compared traditional approaches.

Language: Английский

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