Enhanced battery health monitoring in electric vehicles: A novel hybrid HBA-HGBR model

Journal of Energy Storage, Год журнала: 2025, Номер 110, С. 115316 - 115316

Опубликована: Янв. 8, 2025

Язык: Английский

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GOHBA: Improved Honey Badger Algorithm for Global Optimization

Biomimetics, Год журнала: 2025, Номер 10(2), С. 92 - 92

Опубликована: Фев. 6, 2025

Aiming at the problem that honey badger algorithm easily falls into local convergence, insufficient global search ability, and low convergence speed, this paper proposes a optimization (Global Optimization HBA) (GOHBA), which improves ability of population, with better to jump out optimum, faster stability. The introduction Tent chaotic mapping initialization enhances population diversity initializes quality HBA. Replacing density factor range in entire solution space avoids premature optimum. addition golden sine strategy capability HBA accelerates speed. Compared seven algorithms, GOHBA achieves optimal mean value on 14 23 tested functions. On two real-world engineering design problems, was optimal. three path planning had higher accuracy convergence. above experimental results show performance is indeed excellent.

Язык: Английский

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Optimization method for load aggregation scheduling in industrial parks considering multiple interests and adjustable load classification

Energy, Год журнала: 2025, Номер unknown, С. 135887 - 135887

Опубликована: Апрель 1, 2025

Язык: Английский

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An efficient enhanced exponential distribution optimizer: applications in global, engineering, and combinatorial optimization problems

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Апрель 13, 2025

Язык: Английский

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BINARY HONEY BADGER ALGORITHM ENHANCED WITH TIME-VARYING SIGMOID TRANSFER FUNCTION AND CROSSOVER STRATEGY

Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi :/Osmangazi Üniversitesi Mühendislik-Mimarlık Fakültesi dergisi, Год журнала: 2025, Номер 33(1), С. 1695 - 1711

Опубликована: Апрель 16, 2025

Modeling the foraging behavior of honey badgers, Honey Badger Algorithm (HBA) is a recently proposed metaheuristic algorithm. In this study, binary version algorithm that was for solving continuous optimization problems developed. The S-shaped transfer function and crossover strategy were used to transform into Eight functions with constant time-varying features used, most successful determined. Additionally, effect examined. Three strategies, single-point, two-point, uniform, applied as uniform strategy, which more than others, integrated (BinHBA) developed in way tested on total twenty-seven knapsack problems, fifteen small-scale twelve large-scale. Statistical tests employed analyze results compare them methods found existing literature. showed BinHBA effective preferable.

Язык: Английский

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Reliable identification based intelligent PID tuning for long-period process control under different working conditions

Journal of the Taiwan Institute of Chemical Engineers, Год журнала: 2024, Номер 162, С. 105630 - 105630

Опубликована: Июнь 21, 2024

Язык: Английский

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Fundamental Tradeoffs Between Exploration and Exploitation Search Mechanisms

Emergence, complexity and computation, Год журнала: 2024, Номер unknown, С. 101 - 199

Опубликована: Янв. 1, 2024

Язык: Английский

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A Multi-Strategy Improved Honey Badger Algorithm for Engineering Design Problems

Algorithms, Год журнала: 2024, Номер 17(12), С. 573 - 573

Опубликована: Дек. 13, 2024

A multi-strategy improved honey badger algorithm (MIHBA) is proposed to address the problem that may fall into local optimum and premature convergence when dealing with complex optimization problems. By introducing Halton sequences initialize population, diversity of population enhanced, effectively avoided. The dynamic density factor water waves added improve search efficiency in solution space. Lens opposition learning based on principle lens imaging also introduced enhance ability get rid optimums. MIHBA achieves best ranking 23 test functions 4 engineering design improvement this paper improves speed accuracy algorithm, enhances adaptability solving functions, provides new ideas for

Язык: Английский

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CGJO: a novel complex-valued encoding golden jackal optimization

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

Опубликована: Авг. 23, 2024

Golden jackal optimization (GJO) is inspired by mundane characteristics and collaborative hunting behaviour, which mimics foraging, trespassing encompassing, capturing prey to refresh a jackal's position. However, the GJO has several limitations, such as slow convergence rate, low computational accuracy, premature convergence, poor solution efficiency, weak exploration exploitation. To enhance global detection ability this paper proposes novel complex-valued encoding golden (CGJO) achieve function engineering design. The strategy deploys dual-diploid organization encode real imaginary portions of converts dual-dimensional region single-dimensional manifestation region, increases population diversity, restricts search stagnation, expands area, promotes information exchange, fosters collaboration efficiency improves accuracy. CGJO not only exhibits strong adaptability robustness supplementary advantages but also balances local exploitation promote precision determine best solution. CEC 2022 test suite six real-world designs are utilized evaluate effectiveness feasibility CGJO. compared with three categories existing algorithms: (1) WO, HO, NRBO BKA recently published algorithms; (2) SCSO, GJO, RGJO SGJO highly cited (3) L-SHADE, LSHADE-EpsSin CMA-ES performing algorithms. experimental results reveal that superior those other superiority reliability quicker greater computation precision, stability robustness.

Язык: Английский

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An Enhanced Symmetric Sand Cat Swarm Optimization with Multiple Strategies for Adaptive Infinite Impulse Response System Identification

Symmetry, Год журнала: 2024, Номер 16(10), С. 1255 - 1255

Опубликована: Сен. 24, 2024

An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for modeling remains complicated. The swarm intelligence algorithms facilitate IIR filter’s by exploring parameter domains exploiting acceptable sets. This paper presents an enhanced symmetric sand cat optimization with multiple strategies (MSSCSO) to achieve adaptive identification. principal objective is recognize most regulating coefficients minimize mean square (MSE) between unidentified system’s input output. MSSCSO cooperative swarms integrates ranking-based mutation operator, elite opposition-based learning strategy, simplex method capture supplementary advantages, disrupt regional extreme solutions, identify finest potential solutions. not only receives extensive exploration exploitation refrain from precocious convergence foster computational efficiency; it also endures robustness reliability demographic variability elevate estimation precision. experimental results manifest that practicality feasibility of are superior those other methods in terms speed, calculation precision, detection efficiency, coefficients, MSE fitness value.

Язык: Английский

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