Refining the Eel and Grouper Optimizer with Intelligent Modifications for Global Optimization DOI Creative Commons

Glykeria Kyrou,

Vasileios Charilogis,

Ioannis G. Tsoulos

et al.

Computation, Journal Year: 2024, Volume and Issue: 12(10), P. 205 - 205

Published: Oct. 14, 2024

Global optimization is used in many practical and scientific problems. For this reason, various computational techniques have been developed. Particularly important are the evolutionary techniques, which simulate natural phenomena with aim of detecting global minimum complex A new method Eel Grouper Optimization (EGO) algorithm, inspired by symbiotic relationship foraging strategy eels groupers marine ecosystems. In present work, a series improvements proposed that both at efficiency algorithm to discover total multidimensional functions reduction required execution time through effective number functional evaluations. These modifications include incorporation stochastic termination technique as well an improvement sampling technique. The tested on available from relevant literature compared other methods.

Language: Английский

Multi-Strategy Improved Red-Tailed Hawk Algorithm for Real-Environment Unmanned Aerial Vehicle Path Planning DOI Creative Commons

Mingen Wang,

Panliang Yuan, Pengfei Hu

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(1), P. 31 - 31

Published: Jan. 6, 2025

In recent years, unmanned aerial vehicle (UAV) technology has advanced significantly, enabling its widespread use in critical applications such as surveillance, search and rescue, environmental monitoring. However, planning reliable, safe, economical paths for UAVs real-world environments remains a significant challenge. this paper, we propose multi-strategy improved red-tailed hawk (IRTH) algorithm UAV path real environments. First, enhance the quality of initial population by using stochastic reverse learning strategy based on Bernoulli mapping. Then, is further through dynamic position update optimization mean fusion, which enhances exploration capabilities helps it explore promising solution spaces more effectively. Additionally, proposed an method frontier updates trust domain, better balances exploitation. To evaluate effectiveness algorithm, compare with 11 other algorithms IEEE CEC2017 test set perform statistical analysis to assess differences. The experimental results demonstrate that IRTH yields competitive performance. Finally, validate applicability scenarios, apply path-planning problem practical environments, achieving successfully performing UAVs.

Language: Английский

Citations

1
V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data DOI Creative Commons
Amir Seyyedabbasi, Gang Hu, Hisham A. Shehadeh

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

Citations

1
Enhancing the performance of a additive manufactured battery holder using a coupled artificial neural network with a hybrid flood algorithm and water wave algorithm DOI
Betül Sultan Yıldız

Materials Testing, Journal Year: 2024, Volume and Issue: 66(10), P. 1557 - 1563

Published: Aug. 8, 2024

Abstract This research is the first attempt in literature to combine design for additive manufacturing and hybrid flood algorithms optimal of battery holders an electric vehicle. article uses a recent metaheuristic explore optimization holder A polylactic acid (PLA) material preferred during manufacturing. Specifically, both algorithm (FLA-SA) water wave optimizer (WWO) are utilized generate holder. The hybridized with simulated annealing algorithm. An artificial neural network employed acquire meta-model, enhancing efficiency. results underscore robustness achieving designs car components, suggesting its potential applicability various product development processes.

Language: Английский

Citations

4
Co-movement forecasting between consumer sentiment and stock price in e-commerce platforms using complex network and entropy optimization DOI Creative Commons
Ming-Yue Wang, Rui Kong,

J. Luo

et al.

Frontiers in Physics, Journal Year: 2025, Volume and Issue: 13

Published: March 14, 2025

Stock price and consumer sentiment consistently serve as pivotal economic indicators for the performance growth of e-commerce enterprises. It is essential to comprehend forecast co-movement between two inform financing investment decision-making effectively. Prior research has focused on predicting individual indicators, but not much them attempt their co-movement. We propose a novel Rule Combination based Bivariate Co-movement Network (RC-BCN) approach bivariate forecasting. features extracted utilizing BCN’s topological nature instruct entropy optimization in order enhance RC-BCN’s predictions. conduct four sets experiments 1,135 data from JD.com 2018 2022, where measured using text analysis online reviews. The results indicate that prediction accuracy reaches at most 91% under distortion preference improved by 18% compared without optimization. This study highlights value complex network theory forecasting

Language: Английский

Citations

0
Time Difference Localization Algorithm Based on Iswoa-Pso-Ego DOI
Xinrong Zhang, Bojun Zhang, Li Tai Fang

et al.

Published: Jan. 1, 2025

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

Citations

0
An innovative complex-valued encoding black-winged kite algorithm for global optimization DOI Creative Commons

Chengtao Du,

Jinzhong Zhang, Jie Fang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 6, 2025

Language: Английский

Citations

0
Extreme Learning Machine Optimization Based on Eel and Grouper Optimizer for Gear Fault Diagnosis DOI Open Access

M. K. Harith,

M. Firdaus Isham,

R. Amirulaminnur

et al.

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 2933(1), P. 012018 - 012018

Published: Jan. 1, 2025

Abstract The reliability, dependability, and sustainability of machinery are essential for enhancing industrial productivity efficiency. Any instances machine components failing or malfunctioning can lead to unforeseen downtime financial repercussions. In response, this research introduces a maintenance method mechanical that focuses on diagnosing gear failures through the utilization an extreme learning (ELM) optimization technique known as Eel Grouper Optimizer (EGO). A series vibration signals sourced from online repository, comprised both operational faulty data, were utilized assess proposed methodology. EGO methodology was implemented ascertain optimal configuration ELM approach, specifically focusing determining appropriate number neurons, input weight, bias range values. results suggest strategy improves classification accuracy by 14% compared conventional method. This approach is also transferable other industries seeking enhance dependability their facilities.

Language: Английский

Citations

0
Superb Fairy-wren Optimization Algorithm: a novel metaheuristic algorithm for solving feature selection problems DOI
Heming Jia,

Xuelian Zhou,

Jinrui Zhang

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

0
Multi-strategy improved snake optimizer based on adaptive lévy flight and dual-lens fusion DOI
Guangming Gong, Shengwei Fu, Haisong Huang

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

0
A new neural network–assisted hybrid chaotic hiking optimization algorithm for optimal design of engineering components DOI
Ahmet Remzi Özcan, Pranav Mehta, Sadiq M. Sait

et al.

Materials Testing, Journal Year: 2025, Volume and Issue: unknown

Published: April 11, 2025

Abstract In the era of artificial intelligence (AI), optimization and parametric studies engineering structural systems have become feasible tasks. AI ML (machine learning) offer advantages over classical techniques, which often face challenges such as slower convergence, difficulty handling multiobjective functions, high computational time. Modern techniques may not effectively address all critical design problems despite these advancements. Nature-inspired algorithms based on physical phenomena in nature, human behavior, swarm intelligence, evolutionary principles present a viable alternative for multidisciplinary challenges. This article explores various using newly developed modified hiking algorithm (HOA). The is inspired by hill climbing hiker speed. HOA are compared with those several famous from literature, demonstrating superior results terms statistical measures.

Language: Английский

Citations

0