A hybrid butterfly and Newton–Raphson swarm intelligence algorithm based on opposition-based learning DOI

Chuan Li,

Yanjie Zhu

Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14469 - 14514

Опубликована: Июль 21, 2024

Язык: Английский

A Multi-strategy Slime Mould Algorithm for Solving Global Optimization and Engineering Optimization Problems DOI
Wenchuan Wang,

Wenhui Tao,

Wei-can Tian

и другие.

Evolutionary Intelligence, Год журнала: 2024, Номер 17(5-6), С. 3865 - 3889

Опубликована: Июль 15, 2024

Язык: Английский

Процитировано

3
A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems DOI Creative Commons
Yuting Liu, Hongwei Ding, Zongshan Wang

и другие.

Mathematical Biosciences & Engineering, Год журнала: 2023, Номер 20(9), С. 17242 - 17271

Опубликована: Янв. 1, 2023

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by mixed dynamic mass balance equation on controlled fixed volume. EO has number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness been demonstrated numerical problems. However, the canonical still presents some drawbacks, poor between exploration exploitation operation, tendency to get stuck in local optima low convergence accuracy. To tackle these limitations, this paper proposes new EO-based approach with an adaptive gbest-guided search mechanism chaos (called chaos-based (ACEO)). Firstly, injected enrich population diversity expand range. Next, incorporated enable escape from optima. ACEO 23 classical benchmark functions, compared EO, variants other frontier metaheuristic approaches. experimental results reveal that method remarkably outperforms competitors. In addition, implemented solve mobile robot path planning (MRPP) task, typical techniques. comparison indicates beats competitors, can provide high-quality feasible solutions for MRPP.

Язык: Английский

Процитировано

7
Advances in Slime Mould Algorithm: A Comprehensive Survey DOI Creative Commons

Yuanfei Wei,

Zalinda Othman, Kauthar Mohd Daud

и другие.

Biomimetics, Год журнала: 2024, Номер 9(1), С. 31 - 31

Опубликована: Янв. 4, 2024

The slime mould algorithm (SMA) is a new swarm intelligence inspired by the oscillatory behavior of moulds during foraging. Numerous researchers have widely applied SMA and its variants in various domains field proved value conducting literatures. In this paper, comprehensive review introduced, which based on 130 articles obtained from Google Scholar between 2022 2023. study, firstly, theory described. Secondly, improved are provided categorized according to approach used apply them. Finally, we also discuss main applications SMA, such as engineering optimization, energy machine learning, network, scheduling image segmentation. This presents some research suggestions for interested algorithm, additional multi-objective discrete SMAs extending neural networks extreme learning machining.

Язык: Английский

Процитировано

2
An improved Tasmanian devil optimization algorithm based on sine-cosine strategy with dynamic weighting factors DOI
Huanlong Zhang,

Chenglin Guo,

Jianwei Zhang

и другие.

Cluster Computing, Год журнала: 2024, Номер 27(9), С. 12875 - 12897

Опубликована: Июнь 18, 2024

Язык: Английский

Процитировано

2
A hybrid butterfly and Newton–Raphson swarm intelligence algorithm based on opposition-based learning DOI

Chuan Li,

Yanjie Zhu

Cluster Computing, Год журнала: 2024, Номер 27(10), С. 14469 - 14514

Опубликована: Июль 21, 2024

Язык: Английский

Процитировано

2