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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Bald eagle search algorithm: a comprehensive review with its variants and applications

Systems Science & Control Engineering, Journal Year: 2024, Volume and Issue: 12(1)

Published: Aug. 1, 2024

Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global local searches during optimization, BES effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers comprehensive review research on BES. Beginning with an introduction BES's natural inspiration conceptual framework, it explores modifications, hybridizations, applications domains. Then, critical evaluation performance provided, offering update effectiveness compared recently published algorithms. Furthermore, presents meta-analysis developments outlines potential future directions. As swarm-inspired algorithms become increasingly important tackling complex problems, this study valuable resource for researchers aiming understand algorithms, mainly focusing comprehensively. It investigates evolution, exploring solving intricate fields.

Language: Английский

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Recent applications and advances of African Vultures Optimization Algorithm

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)

Published: Oct. 17, 2024

Language: Английский

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Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning

Biomimetics, Journal Year: 2025, Volume and Issue: 10(1), P. 31 - 31

Published: Jan. 6, 2025

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, environmental monitoring. However, planning reliable, safe, economical paths for UAVs real-world environments remains a significant challenge. this paper, we propose multi-strategy improved red-tailed hawk (IRTH) algorithm UAV path real environments. First, enhance the quality of initial population by using stochastic reverse learning strategy based on Bernoulli mapping. Then, is further through dynamic position update optimization mean fusion, which enhances exploration capabilities helps it explore promising solution spaces more effectively. Additionally, proposed an method frontier updates trust domain, better balances exploitation. To evaluate effectiveness algorithm, compare with 11 other algorithms IEEE CEC2017 test set perform statistical analysis to assess differences. The experimental results demonstrate that IRTH yields competitive performance. Finally, validate applicability scenarios, apply path-planning problem practical environments, achieving successfully performing UAVs.

Language: Английский

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Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 67986 - 68002

Published: Jan. 1, 2024

Node localization is a non-deterministic polynomial time (NP-hard) problem in Wireless Sensor Networks (WSN). It involves determining the geographical position of each node network. For many applications WSNs, such as environmental monitoring, security health and agriculture, precise location nodes crucial. As result this study, we propose novel efficient way to solve without any regard environment, well predetermined conditions. This proposed method based on new Nutcracker Optimization Algorithm (NOA). By utilizing algorithm, it possible maximize coverage rates, decrease numbers, maintain connectivity. Several algorithms were used Grey Wolf (GWO), Kepler Algorithms (KOA), Harris Hawks Optimizer (HHO), Radient-Based (GBO) Gazelle (GOA). The was first tested Istanbul, Turkey, where determined be suitable study area. metaheuristic-based approach distributed architecture, scalable large-scale networks. Among these metaheuristic algorithms, NOA, KOA, GWO have achieved significant performance terms rates (CR), achieving 96.15%, 87.76%, 93.49%, respectively. In their ability sensor problems, proven effective.

Language: Английский

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Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems

Evolutionary Intelligence, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 20, 2024

Language: Английский

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A novel improved horned lizard optimization algorithm to identify optimal parameters of adaptive fuzzy logic MPPT for performance boosting of PEM fuel cell

Intelligent Systems with Applications, Journal Year: 2025, Volume and Issue: 25, P. 200478 - 200478

Published: Jan. 13, 2025

Language: Английский

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A new approach for fire and non-fire aerosols discrimination based on multilayer perceptron trained by modified bonobo optimizer

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

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Attribute reduction based on adaptive neighborhood rough sets and three-way pied kingfisher optimizer

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126618 - 126618

Published: Jan. 1, 2025

Language: Английский

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Multi-Strategy Improved Secretary Bird Optimization Algorithm

Journal of Computer and Communications, Journal Year: 2025, Volume and Issue: 13(01), P. 90 - 107

Published: Jan. 1, 2025

Language: Английский

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