Enhancing Remote Sensing Image Classification Using Arithmetic Optimization Algorithm with Deep Learning Approach DOI

R. Shanthi,

D Balasubramaniam,

R. Venkatesh

et al.

Published: July 24, 2024

Language: Английский

Optimising post-disaster waste collection by a deep learning-enhanced differential evolution approach DOI Creative Commons
Maziar Yazdani, Kamyar Kabirifar, Milad Haghani

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 132, P. 107932 - 107932

Published: Jan. 31, 2024

In the aftermath of natural disasters, efficient waste collection becomes a crucial challenge, owing to dynamic and unpredictable nature generation, coupled with resource constraints. This paper presents an innovative hybrid methodology that synergizes Long Short-Term Memory (LSTM) machine learning Differential Evolution (DE) optimisation augment efforts post-disaster. The approach leverages real-time data forecast generation high accuracy, facilitating development adaptable strategies. Our is designed dynamically update plans in response evolving scenarios, ensuring timely effective decision-making. Field tests conducted earthquake-prone city have demonstrated superior performance this method managing under fluctuating conditions. Moreover, in-depth sensitivity analysis helps identifying key areas for improvement. Significantly outperforming traditional models, offers substantial time savings equips disaster teams robust tool addressing challenges collection.

Language: Английский

Citations

14
A novel hippo swarm optimization: for solving high-dimensional problems and engineering design problems DOI Creative Commons
Guoyuan Zhou,

Jiaxuan Du,

Jia Guo

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 12 - 42

Published: April 10, 2024

Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional engineering challenges. The primary challenge of lies in striking a balance between exploring wide search space focusing on specific regions. Meanwhile, design problems are intricate come with various constraints. This research introduces novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior hippos, designed address real-world HSO encompasses four distinct strategies based hippos different scenarios: starvation search, alpha margination, competition. To assess effectiveness HSO, we conducted experiments using CEC2017 test set, featuring highest dimensional problems, CEC2022 constrained problems. parallel, employed 14 established as control group. experimental outcomes reveal that outperforms well-known algorithms, achieving first average ranking out them CEC2022. Across classical consistently delivers best results. These results substantiate highly effective algorithm for both

Language: Английский

Citations

6
Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives DOI Creative Commons
Amir Aghsami, Simintaj Sharififar, Nader Markazi Moghaddam

et al.

Systems, Journal Year: 2024, Volume and Issue: 12(6), P. 215 - 215

Published: June 18, 2024

Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, vehicles, medical facilities, which can mitigate human casualties address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains research gap in development mathematical models for scenarios, specifically incorporating offices external stakeholders relief centers. Addressing this gap, study examines an optimization model both before after planning humanitarian supply chain logistical framework organization. The areas are defined stakeholders, facilities. A mixed-integer nonlinear is formulated minimize overall costs, considering factors penalty costs untreated injuries demand, delays rescue item distribution operations, waiting injured emergency vehicles air ambulances. implemented using GAMS software 47.1.0 test problems different scales, with Grasshopper Optimization Algorithm proposed larger-scale scenarios. Numerical examples provided show effectiveness feasibility validate metaheuristic approach. Sensitivity analysis conducted assess model’s performance under conditions, key managerial insights implications discussed.

Language: Английский

Citations

4
Time-varying elite sand cat optimisation algorithms for engineering design and feature selection DOI
Li Zhang

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127026 - 127026

Published: March 1, 2025

Language: Английский

Citations

0
An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm DOI Creative Commons
Yanguang Cai,

Changle Guo,

Xiang Chen

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 5, 2024

Language: Английский

Citations

2
An Improved Sand Cat Swarm Optimization with Lens Opposition-based Learning and Sparrow Search Algorithm DOI Creative Commons

Yanguang Cai,

changle guo,

Xiang Chen

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: May 30, 2024

Abstract In order to enhance the global search ability of sand cat swarm optimization, avoid falling into local optimum at a later stage, and improve performance algorithm, an improved algorithm is proposed - Improved optimization based on lens opposition-based learning sparrow (LSSCSO). A dynamic spiral introduced in exploitation stage make for better positions space convergence accuracy algorithm. The are stages jump out capability To evaluate effectiveness LSSCSO solving problems, tested using 23 standard benchmark functions compared with seven competitive algorithms, which show that has strong optimality finding performs optimally most cases. Finally, application four engineering problems also verifies problems.

Language: Английский

Citations

1
Hunger games search algorithm based on stochastic individual information for engineering design optimization problems DOI Creative Commons
Zhen Wang, Dong Zhao, Ali Asghar Heidari

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(3), P. 280 - 307

Published: May 1, 2024

Abstract The hunger games search algorithm (HGS) is a newly proposed metaheuristic that emulates hunger-driven foraging behaviors in population. It combines fitness values to determine individual weights and updates them based on value size, resulting high adaptability effective optimization. However, HGS faces issues like low convergence accuracy susceptibility local optima complex optimization problems. To address these problems, an improved version called BDFXHGS introduced. incorporates collaborative feeding strategy HGS’s design advantages. Individuals approach others degree, facilitating information exchange resolving issues. disperse directional crossover enhance exploration speed. paper conducts qualitative analysis ablation experiments examine the effectiveness of strategies. Comparative are performed using IEEE CEC 2017 benchmark functions compare with competitive algorithms, including previous champion algorithms different dimensions. Additionally, evaluated 25 constrained problems from 2020 competition five real engineering Experimental results show performs well benchmarks outperforms other real-world applications.

Language: Английский

Citations

0
Parameter optimization of the simulated moving bed system based on the IMOSCSO algorithm DOI
Yuhuan Chen, Ling Li

The Canadian Journal of Chemical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 22, 2024

Abstract The optimization of operating parameters for the simulated moving bed (SMB) is complex. A parameter method SMB system was proposed based on improved multi‐objective sand cat swarm (IMOSCSO) algorithm. (MOSCSO) algorithm integrated update and selection mechanism repository in Three strategies were to improve traditional MOSCSO increased population diversity, global search capability, convergence speed. First, cubic chaotic map used initialize population, which uniformity distribution. Second, including a variable spiral strategy prey phase enabled explore more paths adjust its position. Third, speed enhanced by incorporating alert sparrow tested with standard test functions. IMOSCSO outperformed other algorithms terms accuracy. Finally, optimized SMB, demonstrating practical applications.

Language: Английский

Citations

0
Enhancing Remote Sensing Image Classification Using Arithmetic Optimization Algorithm with Deep Learning Approach DOI

R. Shanthi,

D Balasubramaniam,

R. Venkatesh

et al.

Published: July 24, 2024

Language: Английский

Citations

0