Artificial lemming algorithm: a novel bionic meta-heuristic technique for solving real-world engineering optimization problems

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

Опубликована: Янв. 6, 2025

The advent of the intelligent information era has witnessed a proliferation complex optimization problems across various disciplines. Although existing meta-heuristic algorithms have demonstrated efficacy in many scenarios, they still struggle with certain challenges such as premature convergence, insufficient exploration, and lack robustness high-dimensional, nonconvex search spaces. These limitations underscore need for novel techniques that can better balance exploration exploitation while maintaining computational efficiency. In response to this need, we propose Artificial Lemming Algorithm (ALA), bio-inspired metaheuristic mathematically models four distinct behaviors lemmings nature: long-distance migration, digging holes, foraging, evading predators. Specifically, migration burrow are dedicated highly exploring domain, whereas foraging predators provide during process. addition, ALA incorporates an energy-decreasing mechanism enables dynamic adjustments between exploitation, thereby enhancing its ability evade local optima converge global solutions more robustly. To thoroughly verify effectiveness proposed method, is compared 17 other state-of-the-art on IEEE CEC2017 benchmark test suite CEC2022 suite. experimental results indicate reliable comprehensive performance achieve superior solution accuracy, convergence speed, stability most cases. For 29 10-, 30-, 50-, 100-dimensional functions, obtains lowest Friedman average ranking values among all competitor methods, which 1.7241, 2.1034, 2.7241, 2.9310, respectively, 12 again wins optimal 2.1667. Finally, further evaluate applicability, implemented address series cases, including constrained engineering design, photovoltaic (PV) model parameter identification, fractional-order proportional-differential-integral (FOPID) controller gain tuning. Our findings highlight competitive edge potential real-world applications. source code publicly available at https://github.com/StevenShaw98/Artificial-Lemming-Algorithm .

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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Reinforcement learning guided auto-select optimization algorithm for feature selection

Expert Systems with Applications, Год журнала: 2025, Номер 268, С. 126320 - 126320

Опубликована: Янв. 5, 2025

Язык: Английский

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Offline learning-based competitive swarm optimizer for non-linear fixed-charge transportation problems

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101608 - 101608

Опубликована: Май 22, 2024

Язык: Английский

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A multi-stage competitive swarm optimization algorithm for solving large-scale multi-objective optimization problems

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125411 - 125411

Опубликована: Сен. 1, 2024

Язык: Английский

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Improved multi-strategy adaptive Grey Wolf Optimization for practical engineering applications and high-dimensional problem solving

Artificial Intelligence Review, Год журнала: 2024, Номер 57(10)

Опубликована: Сен. 5, 2024

Язык: Английский

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Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection

Computers in Biology and Medicine, Год журнала: 2025, Номер 186, С. 109587 - 109587

Опубликована: Янв. 2, 2025

Язык: Английский

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A model and algorithm for reactive multi-objective multi-skilled project scheduling under resource disruptions

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 111043 - 111043

Опубликована: Март 1, 2025

Язык: Английский

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Load Balancing in Cloud Computing based on Ant Colony Optimization and Crow Search Algorithm

Опубликована: Фев. 16, 2025

Язык: Английский

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Uncertainty handling in learning to rank: a systematic review

International Journal of Data Science and Analytics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 18, 2025

Язык: Английский

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