Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems

Artificial Intelligence Review, Год журнала: 2024, Номер 57(6)

Опубликована: Май 3, 2024

Abstract Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address challenges, swarm intelligence algorithm is introduced as a metaheuristic method effectively implemented. However, existing technology exhibits drawbacks such slow convergence speed, low precision, poor robustness. In this paper, we propose novel approach called Red-billed Blue Magpie Optimizer (RBMO), inspired by cooperative efficient predation behaviors red-billed blue magpies. The mathematical model RBMO was established simulating searching, chasing, attacking prey, food storage magpie. demonstrate RBMO’s performance, first conduct qualitative analyses through behavior experiments. Next, numerical optimization capabilities substantiated using CEC2014 (Dim = 10, 30, 50, 100) CEC2017 suites, consistently achieving best Friedman mean rank. UAV planning applications (two-dimensional three − dimensional), obtains preferable solutions, demonstrating its effectiveness solving NP-hard problems. Additionally, five problems, yields minimum cost, showcasing advantage practical problem-solving. We compare our experimental results categories widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, (3) high-performance optimizers, including CEC winners.

Язык: Английский

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Modified LSHADE-SPACMA with new mutation strategy and external archive mechanism for numerical optimization and point cloud registration

Artificial Intelligence Review, Год журнала: 2025, Номер 58(3)

Опубликована: Янв. 6, 2025

Abstract Numerical optimization and point cloud registration are critical research topics in the field of artificial intelligence. The differential evolution algorithm is an effective approach to address these problems, LSHADE-SPACMA, winning CEC2017, a competitive variant. However, LSHADE-SPACMA’s local exploitation capability can sometimes be insufficient when handling challenges. Therefore, this work, we propose modified version LSHADE-SPACMA (mLSHADE-SPACMA) for numerical registration. Compared original approach, work presents three main innovations. First, present precise elimination generation mechanism enhance algorithm’s ability. Second, introduce mutation strategy based on semi-parametric adaptive rank-based selective pressure, which improves evolutionary direction. Third, elite-based external archiving mechanism, ensures diversity population accelerate convergence progress. Additionally, utilize CEC2014 (Dim = 10, 30, 50, 100) CEC2017 test suites experiments, comparing our against: (1) 10 recent CEC winner algorithms, including LSHADE, EBOwithCMAR, jSO, LSHADE-cnEpSin, HSES, LSHADE-RSP, ELSHADE-SPACMA, EA4eig, L-SRTDE, LSHADE-SPACMA; (2) 4 advanced variants: APSM-jSO, LensOBLDE, ACD-DE, MIDE. results Wilcoxon signed-rank Friedman mean rank demonstrate that mLSHADE-SPACMA not only outperforms but also surpasses other high-performance optimizers, except it inferior L-SRTDE CEC2017. Finally, 25 cases from Fast Global Registration dataset applied simulation analysis potential developed technique solving practical problems. code available at https://github.com/ShengweiFu?tab=repositories https://ww2.mathworks.cn/matlabcentral/fileexchange/my-file-exchange

Язык: Английский

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A multi-strategy enhanced Dung Beetle Optimization for real-world engineering problems and UAV path planning

Alexandria Engineering Journal, Год журнала: 2025, Номер 118, С. 406 - 434

Опубликована: Янв. 24, 2025

Язык: Английский

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Metaheuristic search algorithms in Frequency Constrained Truss Problems: Four improved evolutionary algorithms, optimal solutions and stability analysis

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112854 - 112854

Опубликована: Фев. 1, 2025

Язык: Английский

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A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions

Decision Analytics Journal, Год журнала: 2025, Номер unknown, С. 100551 - 100551

Опубликована: Фев. 1, 2025

Язык: Английский

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DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Computers in Biology and Medicine, Год журнала: 2024, Номер 178, С. 108780 - 108780

Опубликована: Июнь 22, 2024

Язык: Английский

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A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and constrained engineering applications

Heliyon, Год журнала: 2024, Номер 10(10), С. e30757 - e30757

Опубликована: Май 1, 2024

Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is critical need enhance these existing meta-heuristics by employing variety evolutionary techniques tackle emerging challenges in engineering applications. As result, this study attempts boost efficiency recently introduced bio-inspired algorithm, Tunicate Swarm Algorithm (TSA), which motivated foraging and swarming behaviour bioluminescent tunicates residing deep sea. Like other algorithms, TSA has certain limitations, including getting trapped local optimal values lack exploration ability, resulting premature convergence when dealing with highly challenging To overcome shortcomings, novel multi-strategy ameliorated TSA, termed Quasi-Oppositional Chaotic (QOCTSA), proposed as an enhanced variant TSA. This method contributes simultaneous incorporation Based Learning (QOBL) Local Search (CLS) mechanisms effectively balance exploitation. The implementation QOBL improves accuracy rate, while inclusion CLS strategy ten chaotic maps exploitation enhancing search ability around most prospective regions. Thus, QOCTSA significantly enhances maintaining diversification. experimentations are conducted on set thirty-three diverse functions: CEC2005 CEC2019 test functions, well several real-world statistical graphical outcomes indicate that superior exhibits faster rate convergence. Furthermore, tests, specifically Wilcoxon rank-sum t-test, reveal outperforms competing domain design

Язык: Английский

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Information acquisition optimizer: a new efficient algorithm for solving numerical and constrained engineering optimization problems

The Journal of Supercomputing, Год журнала: 2024, Номер 80(18), С. 25736 - 25791

Опубликована: Авг. 14, 2024

Язык: Английский

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Hierarchical parallel search with automatic parameter configuration for particle swarm optimization

Applied Soft Computing, Год журнала: 2023, Номер 151, С. 111126 - 111126

Опубликована: Дек. 7, 2023

Язык: Английский

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Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Measurement Science and Technology, Год журнала: 2024, Номер 35(7), С. 075103 - 075103

Опубликована: Апрель 8, 2024

Abstract Control valves play a vital role in process production. In practical applications, control are prone to blockage and leakage faults. At the small valve openings, vibration signals exhibit drawbacks of significant interference weak fault characteristics, which causes subpar diagnosis performance. To address issue, diagnostic model based on optimized variational mode decomposition (VMD) improved deep belief network-extreme learning machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, adaptive Gaussian–Cauchy mutation strategies applied dung beetle optimization algorithm (DBO) escape local optima. Then, DBO (IDBO) used optimize VMD parameters obtain series modal components. Next, generalized dispersion entropy (GDE) formed by combination Gaussian distribution refined composite multiscale fluctuation-based entropy. The maximum correlation coefficient components extract GDE. Finally, IDBO DBN-ELM network improve classification performance comparative experiment results demonstrate that proposed can effective features accuracy reaches 99.87%.

Язык: Английский

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