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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Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

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

Published: March 30, 2024

Abstract To address the issues of lacking ability, loss population diversity, and tendency to fall into local extreme value in later stage optimization searching, resulting slow convergence lack exploration ability artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a search that integrates positive cosine Cauchy's variance (SCAGTO). Firstly, is initialized using refractive reverse learning mechanism increase species diversity. A strategy nonlinearly decreasing weight factors are introduced finder position update coordinate global algorithm. The follower updated by introducing Cauchy variation perturb optimal solution, thereby improving algorithm's obtain solution. SCAGTO evaluated 30 classical test functions Test Functions 2018 terms speed, accuracy, average absolute error, other indexes, two engineering design problems, namely, pressure vessel problem welded beam problem, for verification. experimental results demonstrate improved significantly enhances speed exhibits good robustness. demonstrates certain solution advantages optimizing verifying superior practicality

Language: Английский

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Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 235 - 235

Published: June 3, 2023

Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With rise development machine learning deep methods, swarm intelligence algorithms have become research direction, combining image with new effective improvement method. Swarm algorithm refers to an intelligent computing method formed by simulating evolutionary laws, behavior characteristics, thinking patterns insects, birds, natural phenomena, other biological populations. It efficient parallel global optimization capabilities strong performance. In this paper, ant colony algorithm, particle sparrow search bat thimble are deeply studied. The model, features, strategies, application fields processing, such as segmentation, matching, classification, feature extraction, edge detection, comprehensively reviewed. theoretical research, analyzed compared. Combined current literature, methods above comprehensive summarized. representative combined segmentation extracted for list analysis summary. Then, unified framework, common different differences summarized, existing problems raised, finally, future trend is projected.

Language: Английский

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A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(6)

Published: May 15, 2024

Abstract In recent years, swarm intelligence optimization algorithms have been proven to significant effects in solving combinatorial problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into form novel has proposed new direction for better The longhorn beetle whisker search algorithm an emerging heuristic algorithm, originates from simulation foraging behavior. This simulates touch strategy required by beetles during foraging, and achieves efficient complex problem spaces through bioheuristic methods. article reviews progress on 2017 present. Firstly, basic principle model structure were introduced, its differences connections with other analyzed. Secondly, this paper summarizes achievements scholars years improvement algorithms. Then, application various fields was explored, including function optimization, engineering design, path planning. Finally, proposes future directions, deep learning fusion, processing multimodal problems, etc. Through review, readers will comprehensive understanding status prospects providing useful guidance practical

Language: Английский

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A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Biomimetics, Journal Year: 2024, Volume and Issue: 9(5), P. 270 - 270

Published: April 28, 2024

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened ability in the process of finding optimal solution. At same time, it is easy to fall into local extremes other limitations. In this paper, a novel (SCMGJO) combining sine–cosine Cauchy mutation proposed. On one hand, tent mapping reverse learning introduced population initialization, sine cosine strategies are update prey positions, which enhances global exploration algorithm. introduction for perturbation solution effectively improves algorithm’s obtain Through experiment 23 benchmark test functions, results show that SCMGJO performs well speed accuracy. addition, stretching/compression spring design problem, three-bar truss unmanned aerial vehicle path planning problem verification. experimental prove superior performance compared with intelligent algorithms verify its application engineering applications.

Language: Английский

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Solving Engineering Optimization Problems Based on Multi-Strategy Particle Swarm Optimization Hybrid Dandelion Optimization Algorithm

Biomimetics, Journal Year: 2024, Volume and Issue: 9(5), P. 298 - 298

Published: May 17, 2024

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. this paper, a multi-strategy particle hybrid dandelion algorithm (PSODO) is proposed, which based on the problems of slow speed being easily susceptible to falling into local extremum ability algorithm. This makes whole more diverse by introducing strong global search unique individual update rules (i.e., rising, landing). The ascending descending stages also help introduce changes explorations space, thus better balancing search. experimental results show that compared with other algorithms, proposed PSODO greatly improves optimal value ability, convergence speed. effectiveness feasibility are verified solving 22 benchmark functions three engineering design different complexities CEC 2005 comparing it algorithms.

Language: Английский

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Novel WSN Coverage Optimization Strategy Via Monarch Butterfly Algorithm and Particle Swarm Optimization

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 135(4), P. 2255 - 2280

Published: April 1, 2024

Language: Английский

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A comprehensive survey on the chicken swarm optimization algorithm and its applications: state-of-the-art and research challenges

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(7)

Published: June 11, 2024

Abstract The application of optimization theory and the algorithms that are generated from it has increased along with science technology's continued advancement. Numerous issues in daily life can be categorized as combinatorial issues. Swarm intelligence have been successful machine learning, process control, engineering prediction throughout years shown to efficient handling An intelligent system called chicken swarm algorithm (CSO) mimics organic behavior flocks chickens. In benchmark problem's objective function, outperforms several popular methods like PSO. concept advancement flock algorithm, comparison other meta-heuristic algorithms, development trend reviewed order further enhance search performance quicken research algorithm. fundamental model is first described, enhanced based on parameters, chaos quantum optimization, learning strategy, population diversity then summarized using both domestic international literature. use group areas feature extraction, image processing, robotic engineering, wireless sensor networks, power. Second, evaluated terms benefits, drawbacks, algorithms. Finally, direction anticipated.

Language: Английский

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Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm

Biomimetics, Journal Year: 2024, Volume and Issue: 9(7), P. 388 - 388

Published: June 26, 2024

The Sine-Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It presented as solution to shortcomings of (TSO) algorithm, which include its tendency reach local optima limited capacity search worldwide. This updates locations using technique greedy approach generates initial solutions an elite reverse learning process. Additionally, it offers individual location called golden sine, enhances algorithm's explore widely steer clear optima. To plan UAV paths safely effectively in complex obstacle environments, SLTSO considers constraints such geographic airspace obstacles, along with performance metrics like environment, space, distance, angle, altitude, threat levels. effectiveness verified by simulation creation path planning model. Experimental results show that displays faster convergence rates, better precision, shorter smoother paths, concomitant reduction energy usage. A drone can now map route far more thanks these improvements. Consequently, proposed demonstrates both efficacy superiority applications.

Language: Английский

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Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application

Biomimetics, Journal Year: 2024, Volume and Issue: 9(10), P. 595 - 595

Published: Oct. 1, 2024

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective issues in recent years. Their study has garnered lot of attention since problems hard high-dimensional goal space. The black-winged kite algorithm still suffers from the imbalance between global search and local development capabilities, it is prone even though combines Cauchy mutation enhance algorithm's ability. heuristic fused with osprey (OCBKA), which initializes population by logistic chaotic mapping fuses improve performance algorithm, proposed means enhancing ability (BKA). By using numerical comparisons CEC2005 CEC2021 benchmark functions, along other swarm solutions three engineering problems, upgraded strategy's efficacy confirmed. Based on experiment findings, revised OCBKA very competitive because can handle complicated high convergence accuracy quick time when compared comparable algorithms.

Language: Английский

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