Multi-Strategy Improved Dung Beetle Optimization Algorithm and Its Applications

Biomimetics, Год журнала: 2024, Номер 9(5), С. 291 - 291

Опубликована: Май 13, 2024

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, unsatisfactory speed when facing complex problems. In response, this paper proposes the multi-strategy improved algorithm (MDBO). core improvements include using Latin hypercube sampling better initialization introduction of novel differential variation strategy, termed "Mean Differential Variation", enhance algorithm's ability evade optima. Moreover, strategy combining lens imaging reverse learning dimension-by-dimension was proposed applied current optimal solution. Through comprehensive performance testing on standard benchmark functions CEC2017 CEC2020, MDBO demonstrates superior in terms accuracy, stability, compared with other classical metaheuristic algorithms. Additionally, efficacy addressing real-world engineering problems validated through three representative application scenarios namely extension/compression spring design problems, reducer welded beam

Язык: Английский

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

Artificial Intelligence Review, Год журнала: 2024, Номер 57(9)

Опубликована: Авг. 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

Язык: Английский

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Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 434, С. 117588 - 117588

Опубликована: Ноя. 29, 2024

Язык: Английский

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Balanced dung beetle optimization algorithm based on parameter substitution and escape strategy

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 17, 2025

Dung Beetle algorithm is an intelligent optimization with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there imbalance between exploration ability, it easy fall into problem local optimal solution. The purpose this study improve performance dung beetle explore its engineering application value. A balanced was proposed, parabolic adaptive parameter R introduced broaden range slow down convergence. Gaussian distributed phase β reduce parameters stimulate potential exploitation. Levy flight escape strategy balance global ability fully solution space. effectiveness improved verified by comparing CEC2017 benchmark function single variant. experimental results show that BDBO superior algorithms terms accuracy generalization improvement percentage 35.29% compared DBO algorithm. Wilcoxon rank sum test used evaluate results, which proved statistically significant. Finally, applied tracking technology maximum power point photovoltaic system, effect better has more

Язык: Английский

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An approach for multipath optimal selection of network service combinations based on golden eagle optimizer with double learning strategies

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2025, Номер 14(1)

Опубликована: Янв. 30, 2025

Язык: Английский

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Enhanced crayfish optimization algorithm: Orthogonal refracted opposition-based learning for robotic arm trajectory planning

PLoS ONE, Год журнала: 2025, Номер 20(2), С. e0318203 - e0318203

Опубликована: Фев. 5, 2025

In high-dimensional scenarios, trajectory planning is a challenging and computationally complex optimization task that requires finding the optimal within domain. Metaheuristic (MH) algorithms provide practical approach to solving this problem. The Crayfish Optimization Algorithm (COA) an MH algorithm inspired by biological behavior of crayfish. However, COA has limitations, including insufficient global search capability tendency converge local optima. To address these challenges, Enhanced (ECOA) proposed for robotic arm planning. ECOA incorporates multiple novel strategies, using tent chaotic map population initialization enhance diversity replacing traditional step size adjustment with nonlinear perturbation factor improve capability. Furthermore, orthogonal refracted opposition-based learning strategy enhances solution quality efficiency leveraging dominant dimensional information. Additionally, performance comparisons eight advanced on CEC2017 test set (30-dimensional, 50-dimensional, 100-dimensional) are conducted, ECOA’s effectiveness validated through Wilcoxon rank-sum Friedman mean rank tests. experiments, demonstrated superior performance, reducing costs 15% compared best competing 10% over original COA, significantly lower variability. This demonstrates improved quality, robustness, convergence stability. study successfully introduces strategies improvement, as well verification in path results confirm potential challenges various engineering applications.

Язык: Английский

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Sturnus vulgaris escape algorithm and its application to mechanical design

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 24, 2025

Practical engineering optimization problems are characterized by high dimensionality, non-convexity, and non-linearity, the use of optimizers to provide better quality solutions target problem in an acceptable time is a hot research topic field optimal design. In this paper, inspired Sturnus vulgaris escape behavior, Vulgaris Escape Algorithm (SVEA) proposed high-performance optimizer for complex problems. The algorithm composed exploration exploitation strategies, controlled fixed parameters. strategies include High-Altitude Strategy Wave 1, while consist Cordon Line 2. enhances capabilities reorganizing subgroups, preventing leader individuals from overlapping, avoiding collisions between individuals. conducts refined searches around high-value regions, further improving precision. Strategies 1 2 help population local optima prevent over-spreading. performance SVEA evaluated through employment 23 benchmark test functions CEC2017 set, with subsequent comparison undertaken nine statE − of-thE art meta-heuristic algorithms. outcomes evaluation demonstrate that attains top ranking identified as best-performing across all sets. A statistical analysis reveals solution set exhibits superior other algorithms, discrepancy being deemed be statistically significant. Finally, applied five real-world problems, providing satisfying constraints.

Язык: Английский

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Research on monthly runoff prediction model considering secondary decomposition of multiple fitness functions and deep learning

Geomatics Natural Hazards and Risk, Год журнала: 2025, Номер 16(1)

Опубликована: Фев. 27, 2025

Язык: Английский

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Time-varying elite sand cat optimisation algorithms for engineering design and feature selection

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127026 - 127026

Опубликована: Март 1, 2025

Язык: Английский

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