Energy management strategy for methanol hybrid commercial vehicles based on improved dung beetle algorithm optimization

PLoS ONE, Год журнала: 2025, Номер 20(1), С. e0313303 - e0313303

Опубликована: Янв. 2, 2025

In order to solve the problem of poor adaptability and robustness rule-based energy management strategy (EMS) in hybrid commercial vehicles, leading suboptimal vehicle economy, this paper proposes an improved dung beetle algorithm (DBO) optimized multi-fuzzy control EMS. First, EMS is established by dividing efficient working areas methanol engine power battery. The Tent chaotic mapping then used integrate strategies cosine, Lévy flight, Cauchy Gaussian mutation, improving DBO. This integration compensates for traditional algorithm’s tendency fall into local optima enhances its global search capability. Subsequently, fuzzy controllers driving charging mode are designed under Finally, DBO obtain optimal controller taking fuel consumption whole fluctuation change battery state charge ( SOC ) as optimization objectives. Compared strategies, using enhanced continuously adjusts torque distribution between motor based on vehicle’s real-time state, resulting a 9.07% reduction 3.43% decrease fluctuations.

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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Improved Cyclic System Based Optimization Algorithm (ICSBO)

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2025, Номер 0(0), С. 1 - 10

Опубликована: Янв. 1, 2025

Cyclic-system-based optimization (CSBO) is an innovative metaheuristic algorithm (MHA) that draws inspiration from the workings of human blood circulatory system.However, CSBO still faces challenges in solving complex problems, including limited convergence speed and a propensity to get trapped local optima.To improve performance further, this paper proposes improved cyclic-system-based (ICSBO).First, venous circulation, adaptive parameter changes with evolution introduced balance between diversity stage enhance exploration search space.Second, simplex method strategy incorporated into systemic pulmonary circulations, which improves update formulas.A learning aimed at optimal individual, combined straightforward opposition-based approach, employed population while preserving diversity.Finally, novel external archive utilizing supplementation mechanism diversity, maximize use superior genes, lower risk being optima.Testing on CEC2017 benchmark set shows compared original eight other outstanding MHAs, ICSBO demonstrates remarkable advantages speed, precision, stability.

Язык: Английский

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A New Hybrid Improved Kepler Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications

Mathematics, Год журнала: 2025, Номер 13(3), С. 405 - 405

Опубликована: Янв. 26, 2025

The Kepler optimization algorithm (KOA) is a metaheuristic based on Kepler’s laws of planetary motion and has demonstrated outstanding performance in multiple test sets for various issues. However, the KOA hampered by limitations insufficient convergence accuracy, weak global search ability, slow speed. To address these deficiencies, this paper presents multi-strategy fusion (MKOA). Firstly, initializes population using Good Point Set, enhancing diversity. Secondly, Dynamic Opposition-Based Learning applied individuals to further improve its exploration effectiveness. Furthermore, we introduce Normal Cloud Model perturb best solution, improving rate accuracy. Finally, new position-update strategy introduced balance local search, helping escape optima. MKOA, uses CEC2017 CEC2019 suites testing. data indicate that MKOA more advantages than other algorithms terms practicality Aiming at engineering issue, study selected three classic cases. results reveal demonstrates strong applicability practice.

Язык: Английский

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An enhanced dung beetle optimizer with multiple strategies for robot path planning

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

Опубликована: Фев. 7, 2025

In order to make up for the shortcomings of original dung beetle optimization algorithm, such as low population diversity, insufficient global exploration ability, being easy fall into local and unsatisfactory convergence accuracy, etc. An improved algorithm using hybrid multi- strategy is proposed. Firstly, cubic chaotic mapping approach used initialize improve expand search range solution space, enhance ability. Secondly, cooperative utilized strength communication between individual beetles groups in foraging stage space Thirdly, T-distribution mutation differential evolutionary variation strategies are introduced provide perturbation diversity avoid falling optimization. Fourthly, proposed algorithm(named SSTDBO) compared with other algorithms, including GODBO, QHDBO, DBO, KOA, NOA, WOA HHO, by 29 benchmark test functions CEC2017. The results show that has stronger robustness algorithm's performance substantially enhanced. Finally, applied solve real-world robot path planning engineering cases, demonstrate its effectiveness dealing real which further verified how noteworthy enhanced strategy's efficacy superiority addressing cases.

Язык: Английский

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Hybrid Modeling of Catalytic Cracking Processes Based on the 17-Lump Kinetic Model and Light Gradient Boosting Machine

Energy & Fuels, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Fluid catalytic cracking is a crucial procedure in the petrochemical industry, but understanding and predicting its process remains challenging due to high degree of complexity nonlinearities. This paper proposes hybrid model incorporating 17-lump kinetic (SLKM) light gradient boosting machine (LightGBM). First, as difficulty parameter determination SLKM, this improves dung beetle optimization algorithm for solving parameters SLKM. The simulates search behavior insects uses fitness function optimize parameters, thereby addressing inaccuracy time-consuming nature mechanistic model. Second, challenge hyperparameter LightGBM, which greatly influences precision predictions, addressed by applying Bayesian with Hyperband. prediction performance LightGBM significantly following optimization. backed existing theoretical knowledge, produces outputs consistent physicochemical laws requires less data, though limited. To enhance performance, we construct three models combining SLKM LightGBM: series model, parallel adaptive weights, realizing complementarity both models. industrial data validation results demonstrate that weighted achieves best mean relative error, squared absolute error reaching 1.36%, 0.0169, 0.077 on plant set, respectively.

Язык: Английский

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Colonial bacterial memetic algorithm and its application on a darts playing robot

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

Опубликована: Март 28, 2025

In this paper, we present the Colonial Bacterial Memetic Algorithm (CBMA), an advanced evolutionary optimization approach for robotic applications. CBMA extends by integrating Cultural Algorithms and co-evolutionary dynamics inspired bacterial group behavior. This combination of natural artificial elements results in a robust algorithm capable handling complex challenges robotics, such as constraints, multiple objectives, large search spaces, models, while delivering fast accurate solutions. incorporates features like multi-level clustering, dynamic gene selection, hierarchical population adaptive mechanisms, enabling efficient management task-specific parameters optimizing solution quality minimizing resource consumption. The algorithm's effectiveness is demonstrated through real-world application, achieving 100% success rate robot arm's ball-throwing task usually with significantly fewer iterations evaluations compared to other methods. was also evaluated using CEC-2017 benchmark suite, where it consistently outperformed state-of-the-art algorithms, superior outcomes 71% high-dimensional cases demonstrating up 80% reduction required evaluations. These highlight CBMA's efficiency, adaptability, suitability specialized tasks. Overall, exhibits exceptional performance both evaluations, effectively balancing exploration exploitation, representing significant advancement robotics.

Язык: Английский

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An efficient semi-automated characterization of rock mass discontinuities from 3D point clouds based on Nutcracker Optimization Algorithm-improved probabilistic neural network

Bulletin of Engineering Geology and the Environment, Год журнала: 2025, Номер 84(4)

Опубликована: Март 31, 2025

Язык: Английский

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Research on Move-to-Escape Enhanced Dung Beetle Optimization and Its Applications

Biomimetics, Год журнала: 2024, Номер 9(9), С. 517 - 517

Опубликована: Авг. 29, 2024

The dung beetle optimization (DBO) algorithm is acknowledged for its robust capabilities and rapid convergence as an efficient swarm intelligence technique. Nevertheless, DBO, similar to other algorithms, often gets trapped in local optima during the later stages of optimization. To mitigate this challenge, we propose Move-to-Escape (MEDBO) paper. MEDBO utilizes a good point set strategy initializing swarm's initial population, ensuring more uniform distribution diminishing risk entrapment. Moreover, it incorporates factors dynamically balances number offspring foraging individuals, prioritizing global exploration initially subsequently. This dynamic adjustment not only enhances search speed but also prevents stagnation. algorithm's performance was assessed using CEC2017 benchmark suite, which confirmed MEDBO's significant improvements. Additionally, applied three engineering problems: pressure vessel design, three-bar truss spring design. exhibited excellent these applications, demonstrating practicality efficacy real-world problem-solving contexts.

Язык: Английский

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Cold Chain Logistics Center Layout Optimization Based on Improved Dung Beetle Algorithm

Symmetry, Год журнала: 2024, Номер 16(7), С. 805 - 805

Опубликована: Июнь 27, 2024

To reduce the impact of cold chain logistics center layout on economic benefits, operating efficiency and carbon emissions, a optimization method is proposed based improved dung beetle algorithm. Firstly, analysis relationship between non-logistics, multi-objective model established to minimize total cost, maximize adjacency correlation emissions; secondly, standard Dung Beetle Optimization (DBO) algorithm, in order further improve global exploration ability Chebyshev chaotic mapping an adaptive Gaussian–Cauchy hybrid mutation disturbance strategy are introduced DBO (IDBO) algorithm; finally, taking actual as example, algorithm applied optimize its layout, respectively. The results show that cost after IDBO reduced by 25.54% compared with original 29.93%, emission 6.75%, verifying effectiveness providing reference for design centers.

Язык: Английский

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