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

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(3)

Published: Jan. 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 .

Language: Английский

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An in-depth survey of the artificial gorilla troops optimizer: outcomes, variations, and applications

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(9)

Published: Aug. 12, 2024

Abstract A recently developed algorithm inspired by natural processes, known as the Artificial Gorilla Troops Optimizer (GTO), boasts a straightforward structure, unique stabilizing features, and notably high effectiveness. Its primary objective is to efficiently find solutions for wide array of challenges, whether they involve constraints or not. The GTO takes its inspiration from behavior in world. To emulate impact gorillas at each stage search process, employs flexible weighting mechanism rooted concept. exceptional qualities, including independence derivatives, lack parameters, user-friendliness, adaptability, simplicity, have resulted rapid adoption addressing various optimization challenges. This review dedicated examination discussion foundational research that forms basis GTO. It delves into evolution this algorithm, drawing insights 112 studies highlight Additionally, it explores proposed enhancements GTO’s behavior, with specific focus on aligning geometry area real-world problems. also introduces solver, providing details about identification organization, demonstrates application scenarios. Furthermore, provides critical assessment convergence while limitation In conclusion, summarizes key findings study suggests potential avenues future advancements adaptations related

Language: Английский

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Improved Aquila optimizer and its applications

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

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Aviation gas turbine engine bearings faults diagnosis method based on multi-parameter fusion criterion judgment and AO-PNN

Structural Health Monitoring, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

The diagnosis of bearings is greatly difficult due to strong background noise and complex transmission paths. So, we designed an aviation gas turbine engine fault method. It based on the combination Wavelet packet decomposition (WPD) correlation coefficient-energy ratio-kurtosis criterion judgments with AO-PNN, a probabilistic neural network (PNN) optimized by introducing Aquila optimizer (AO). vibration signal firstly decomposed WPD reconstructed screening Node components judgment. overlapping segmentation signals multi-scale permutation entropy each sample are calculated as feature vector reduced Kernel principal component analysis. AO-PNN used for classification patterns. experimental results show that this method can effectively eliminate interference improve accuracy diagnosis. Compared non-optimized PNN, improved 11.25%.

Language: Английский

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A multi-strategy improved beluga whale optimization algorithm for constrained engineering problems

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14685 - 14727

Published: July 29, 2024

Language: Английский

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Chaos-BBO: Chaos balanced butterfly optimizer with dynamic continuum chaotic strategies and its applications

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(9), P. 11911 - 11952

Published: July 30, 2024

Language: Английский

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Multi-strategy arithmetic optimization algorithm for global optimization and uncertain motion tracking

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(1)

Published: Oct. 17, 2024

Language: Английский

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