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

Advances in Engineering Software, Год журнала: 2025, Номер 202, С. 103866 - 103866

Опубликована: Янв. 30, 2025

Язык: Английский

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An enhanced ivy algorithm fusing multiple strategies for global optimization problems

Advances in Engineering Software, Год журнала: 2025, Номер 203, С. 103862 - 103862

Опубликована: Фев. 6, 2025

Язык: Английский

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

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 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.

Язык: Английский

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

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Май 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.

Язык: Английский

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

Cluster Computing, Год журнала: 2024, Номер 28(2)

Опубликована: Ноя. 26, 2024

Язык: Английский

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

Biomimetics, Год журнала: 2024, Номер 9(8), С. 478 - 478

Опубликована: Авг. 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.

Язык: Английский

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MAHACO: Multi-algorithm hybrid ant colony optimizer for 3D path planning of a group of UAVs

Information Sciences, Год журнала: 2024, Номер unknown, С. 121714 - 121714

Опубликована: Ноя. 1, 2024

Язык: Английский

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mESC: An Enhanced Escape Algorithm Fusing Multiple Strategies for Engineering Optimization

Biomimetics, Год журнала: 2025, Номер 10(4), С. 232 - 232

Опубликована: Апрель 8, 2025

A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address challenges balancing exploration and development stages low convergence accuracy in (ESC). Firstly, an adaptive perturbation factor strategy was employed maintain population diversity. Secondly, introducing a restart mechanism enhance capability mESC. Finally, dynamic centroid reverse learning designed balance local development. In addition, order accelerate global speed, boundary adjustment based on elite pool proposed, which selects individuals replace bad individuals. Comparing mESC with latest metaheuristic high-performance winner CEC2022 testing suite, numerical results confirmed that outperforms other competitors. superiority handling problems verified through several classic real-world optimization problems.

Язык: Английский

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Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy Storage System Capacity Optimization

Biomimetics, Год журнала: 2025, Номер 10(4), С. 244 - 244

Опубликована: Апрель 16, 2025

To address the drawbacks of traditional snake optimization method, such as a random population initialization, slow convergence speed, and low accuracy, an adaptive t-distribution mixed mutation strategy is proposed. Initially, Tent-based chaotic mapping quasi-reverse learning approach are utilized to enhance quality initial solution initialization process original method. During evolution stage, novel foraging introduced substitute stage This perturbs mutates at optimal position generate new solutions, thereby improving algorithm’s ability escape local optima. The mating mode in replaced with opposite-sex attraction mechanism, providing algorithm more opportunities for global exploration exploitation. improved method accelerates improves accuracy while balancing exploitation capabilities. experimental results demonstrate that outperforms other methods, including standard technique, terms robustness accuracy. Additionally, each improvement technique complements amplifies effects others.

Язык: Английский

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

Evolutionary Intelligence, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 9, 2025

Язык: Английский

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