Sub-population evolutionary particle swarm optimization with dynamic fitness-distance balance and elite reverse learning for engineering design problems

Advances in Engineering Software, Journal Year: 2025, Volume and Issue: 202, P. 103866 - 103866

Published: Jan. 30, 2025

Language: Английский

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An enhanced ivy algorithm fusing multiple strategies for global optimization problems

Advances in Engineering Software, Journal Year: 2025, Volume and Issue: 203, P. 103862 - 103862

Published: Feb. 6, 2025

Language: Английский

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DEMFFA: a multi-strategy modified Fennec Fox algorithm with mixed improved differential evolutionary variation strategies

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: May 8, 2024

Abstract The Fennec Fox algorithm (FFA) is a new meta-heuristic that primarily inspired by the fox's ability to dig and escape from wild predators. Compared with other classical algorithms, FFA shows strong competitiveness. “No free lunch” theorem an has different effects in face of problems, such as: when solving high-dimensional or more complex applications, there are challenges as easily falling into local optimal slow convergence speed. To solve this problem FFA, paper, improved Fenna fox DEMFFA proposed adding sin chaotic mapping, formula factor adjustment, Cauchy operator mutation, differential evolution mutation strategies. Firstly, mapping strategy added initialization stage make population distribution uniform, thus speeding up Secondly, order expedite speed algorithm, adjustments made factors whose position updated first stage, resulting faster convergence. Finally, prevent getting too early expand search space population, after second stages original update. In verify performance DEMFFA, qualitative analysis carried out on test sets, tested newly algorithms three sets. And we also CEC2020. addition, applied 10 practical engineering design problems 24-bar truss topology optimization problem, results show potential problems.

Language: Английский

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A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 19, 2024

Abstract For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a model which uses empirical or semi-empirical equations estimate unknown parameters through optimization techniques. This thesis calculates, analyzes discusses sum squares error (SSE) between measured estimated current voltage values using derived from multiple techniques for six commercially available PEMFCs: BCS 500 W-PEMFC, W SR-12 PEMFC, Nedstack PS6 H-12 HORIZON PEMFC 250 W-stack PEMFC. To minimize SSE under these new models we employ an advanced version Artificial Rabbits Optimization called Mutational Northern goshawk Elite opposition learning-based Optimizer (MNEARO). Additionally SSE, Absolute Error (AE), Mean Bias (MBE) are computed different recent methods according literature on measurement. Other algorithms including ARO, TLBO, DE SSA used comparative analysis purposes. On top that MNEARO outperforms others in terms both computational cost as well solution quality while experiments carried out benchmark problems indicate its superiority over other meta-heuristics approaches.

Language: Английский

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Improved exponential distribution optimizer: enhancing global numerical optimization problem solving and optimizing machine learning paramseters

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(2)

Published: Nov. 26, 2024

Language: Английский

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A Multi-Strategy Improvement Secretary Bird Optimization Algorithm for Engineering Optimization Problems

Biomimetics, Journal Year: 2024, Volume and Issue: 9(8), P. 478 - 478

Published: Aug. 8, 2024

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops multi-strategy improvement (MISBOA) to further enhance the solving accuracy and convergence speed for engineering problems. Firstly, feedback regulation mechanism based incremental PID control is used update whole population according output value. Then, in hunting stage, golden sinusoidal guidance strategy employed success rate of capture. Meanwhile, keep diverse, cooperative camouflage an cosine similarity are introduced into escaping stage. Analyzing results CEC2022 test suite, MISBOA both get best comprehensive performance when dimensions set as 10 20. Especially dimension increased, advantage expanded, which ranks first functions, accounting 83.33% total. It illustrates introduction strategies that effectively searching stability various For five real-world problems, also has fitness values, indicating stronger ability with higher stability. Finally, it solve shape problem combined quartic generalized Ball interpolation (CQGBI) curve, can be designed smoother obtained parameters improve power generation efficiency.

Language: Английский

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Multi-strategy enhanced artificial rabbit optimization algorithm for solving engineering optimization problems

Evolutionary Intelligence, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 9, 2025

Language: Английский

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Application of an Improved Differential Evolution Algorithm in Practical Engineering

Concurrency and Computation Practice and Experience, Journal Year: 2025, Volume and Issue: 37(3)

Published: Jan. 20, 2025

ABSTRACT The differential evolution algorithm, as a simple yet effective random search often faces challenges in terms of rapid convergence and sharp decline population diversity during the evolutionary process. To address this issue, an improved namely multi‐population collaboration (MPC‐DE) is introduced article. algorithm proposes mechanism two‐stage mutation operator. Through mechanism, individuals involved effectively controlled, enhancing algorithm's global capability. operator efficiently balances requirements exploration exploitation stages. Additionally, perturbation to enhance ability escape local optima improve stability. By conducting comprehensive comparisons with 15 well‐known optimization algorithms on CEC2005 CEC2017 test functions, MPC‐DE thoroughly evaluated solution accuracy, convergence, stability, scalability. Furthermore, validation 57 real‐world engineering problems CEC2020 demonstrates robustness MPC‐DE. Experimental results reveal that, compared other algorithms, exhibits superior accuracy both constrained unconstrained problems. These research findings provide strong support for widespread applicability addressing practical

Language: Английский

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Enhancing monthly runoff prediction: a data-driven framework integrating variational mode decomposition, enhanced artificial rabbit optimization, support vector regression, and error correction

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 17, 2025

Language: Английский

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Harnessing dynamic turbulent dynamics in parrot optimization algorithm for complex high-dimensional engineering problems

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 440, P. 117908 - 117908

Published: March 19, 2025

Language: Английский

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