Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems DOI Creative Commons
Hussam N. Fakhouri,

Mohannad S. Alkhalaileh,

Faten Hamad

et al.

Algorithms, Journal Year: 2024, Volume and Issue: 17(12), P. 589 - 589

Published: Dec. 20, 2024

This study presents an innovative hybrid evolutionary algorithm that combines the Arctic Puffin Optimization (APO) with JADE dynamic differential evolution framework. The APO algorithm, inspired by foraging patterns of puffins, demonstrates certain challenges, including a tendency to converge prematurely at local minima, slow rate convergence, and insufficient equilibrium between exploration exploitation processes. To mitigate these drawbacks, proposed approach incorporates features JADE, which enhances exploration–exploitation trade-off through adaptive parameter control use external archive. By synergizing effective search mechanisms modeled after behavior puffins JADE’s advanced strategies, this integration significantly improves global efficiency accelerates convergence process. effectiveness APO-JADE is demonstrated benchmark tests against well-known IEEE CEC 2022 unimodal multimodal functions, showing superior performance over 32 compared optimization algorithms. Additionally, applied complex engineering design problems, structures mechanisms, revealing its practical utility in navigating challenging, multi-dimensional spaces typically encountered real-world problems. results confirm outperformed all optimizers, effectively addressing challenges unknown areas optimization.

Language: Английский

Self-adaptive polynomial mutation in NSGA-II DOI
Jose L. Carles-Bou, Severino F. Galán

Soft Computing, Journal Year: 2023, Volume and Issue: 27(23), P. 17711 - 17727

Published: Aug. 21, 2023

Language: Английский

Citations

12
Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application DOI Creative Commons

Yindi Ma,

Yanhai Li, Longquan Yong

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(10), P. 1596 - 1596

Published: May 20, 2024

This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from blended learning model, specifically designed to tackle high-dimensional multimodal complex problems. Firstly, perturbation conditions in “teaching” and “learning” stages original TLBO algorithm are interpreted geometrically, based on search capability is enhanced by adjusting range values random numbers. Second, strategic restructuring has been ingeniously implemented, dividing into three distinct phases: pre-course self-study, classroom learning, post-course consolidation; this structural reorganization crossover strategy self-learning phase effectively enhance global TLBO. To evaluate its performance, BLTLBO was tested alongside seven distinguished variants thirteen functions CEC2014 suite. Furthermore, two excellent algorithms were added comparison mode five scalable CEC2008 The empirical results illustrate algorithm’s superior efficacy handling challenges. Finally, portfolio problem successfully addressed using thereby validating practicality effectiveness proposed method.

Language: Английский

Citations

3
Novel 3D UAV Path Planning for IoT Services Based on Interactive Cylindrical Vector Teaching–Learning Optimization Algorithm DOI Creative Commons
Xinghe Jiang, Xuanyu Wu,

Zhifeng Zhang

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2407 - 2407

Published: April 10, 2025

In the 6G-IoT convergence ecosystem, UAV path planning for static environments is systematically investigated as a resource coordination problem where communication demands and terrain constraints are balanced through intelligent trajectory optimization. The innovation of this paper lies in proposal an interactive cylinder vector teaching–learning-based optimization (ICVTLBO) algorithm, points represented cylindrical coordinates, targeted strategies proposed during teacher learner phases to address uncertainty challenges, such elevation fluctuations link instability caused by obstacles environments. ICVTLBO compared with other classical novel algorithms on CEC2022 benchmark function suite, demonstrating its effectiveness reliability solving complex problems. Subsequently, real digital model (DEM) maps utilized establish nine diverse scenarios simulation 3D experimental results show that outperforms methods, providing high-quality paths UAVs

Language: Английский

Citations

0
Solving distributed low carbon scheduling problem for large complex equipment manufacturing using an improved hybrid artificial bee colony algorithm DOI
Wenxiang Xu, Lei Wang, Dezheng Liu

et al.

Journal of Intelligent & Fuzzy Systems, Journal Year: 2023, Volume and Issue: 45(1), P. 147 - 175

Published: April 21, 2023

Multi-agent collaborative manufacturing, high energy consumption and pollution, frequent operation outsourcing are the three main characteristics of large complex equipment manufacturing enterprises. Therefore, production scheduling problem to be studied is a distributed flexible job shop involving (Oos-DFJSP). Besides, influences each machine on carbon emission at different processing speeds also involved in this research. Thus Oos-DFJSP consists following four sub-problems: determining sequence operations, assigning jobs manufactories, operations machines speed machine. In Oos-DFJSP, if assigned manufactory group enterprise, cannot complete some workpiece, then these will other manufactories with related capabilities. Aiming solving problem, multi-objective mathematical model including costs, makespan was established, which consumption, power generation waste heat treatment capacity pollutants were considered calculation emission. Then, improved hybrid genetic artificial bee colony algorithm developed address above model. Finally, 45 groups random comparison experiments presented. Results indicate that performs better than algorithms not only quality non-dominated solutions but Inverse Generational Distance Error Ratio. That is, proposed proved an excellent method for Oos-DFJSP.

Language: Английский

Citations

8
An improved slime mould algorithm using multiple strategies DOI

Mozhong Zhu,

Rongkun Zhu,

Feng Li

et al.

International Journal of Parallel Emergent and Distributed Systems, Journal Year: 2024, Volume and Issue: 39(4), P. 461 - 485

Published: May 13, 2024

Aiming at the defects of standard slime mould algorithm (SMA), such as local optima stagnation, slow convergence and improper balance between exploitation exploration, we propose an improved SMA that contains adaptive t-distributed variation strategy, location update formula chaotic opposition-based learning is, MISMA. Utilizing comparative experiments ablation studies on classical benchmark CEC2020 suite, proved MISMA outperforms other state-of-the-art rival algorithms speed, solution accuracy, robustness, each component achieves improvement stage exhibits synergistic effects.

Language: Английский

Citations

3
Optimal equivalent circuit models for photovoltaic cells and modules using multi-source guided teaching–learning-based optimization DOI Creative Commons

Y.M. Li,

Guojiang Xiong, Seyedali Mirjalili

et al.

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: 15(11), P. 102988 - 102988

Published: Aug. 5, 2024

The complexity of equivalent circuit models photovoltaic cells and modules poses a difficult task to the parameter extraction methods. Teaching-learning-based optimization (TLBO) is potent metaheuristic-based method, but it suffers from insufficient precision low dependability. This study presented multi-source guided TLBO through improving its two phases. A approach with one-to-one step-by-step teaching strategies was designed guide different learners in teacher phase. Besides, based on multiple were introduced for knowledge reserves strengthen information exchanging. With improvements, advantageous lessen likelihood hitting local optimum thereby global convergence can be accelerated. resultant method verified single diode model, double three additional modules. findings demonstrate that obtained better solutions dependability, stood out crowd algorithms.

Language: Английский

Citations

3
Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves DOI
Gang Hu, Rui Yang, Guo Wei

et al.

Mathematics and Computers in Simulation, Journal Year: 2022, Volume and Issue: 206, P. 709 - 769

Published: Dec. 21, 2022

Language: Английский

Citations

14
A multilevel biomedical image thresholding approach using the chaotic modified cuckoo search DOI
Shouvik Chakraborty, Kalyani Mali

Soft Computing, Journal Year: 2023, Volume and Issue: 28(6), P. 5359 - 5436

Published: Oct. 26, 2023

Language: Английский

Citations

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

Yuanfei Wei,

Zalinda Othman, Kauthar Mohd Daud

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(1), P. 31 - 31

Published: Jan. 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.

Language: Английский

Citations

2
A novel framework for optimizing Gurney flaps using RBF surrogate model and cuckoo search algorithm DOI
Aryan Tyagi, Paras Nath Singh, Aryaman Rao

et al.

Acta Mechanica, Journal Year: 2024, Volume and Issue: 235(6), P. 3385 - 3404

Published: March 13, 2024

Language: Английский

Citations

2