Integrated Q-Learning with Firefly Algorithm for Transportation Problems

被引：0
作者：
Pratiba K.R. [1 ]
Ridhanya S. [1 ]
Ridhisha J. [1 ]
Hemashree P. [1 ]
机构：
[1] Department of Artificial Intelligence and Machine Learning, Coimbatore Institute of Technology, TamilNadu, Coimbatore
来源：
EAI Endorsed Transactions on Energy Web | 2024年 / 11卷
关键词：
Ant Colony Optimization Algorithm; Firefly Algorithm; Genetic Algorithm; Particle Swarm Optimization; Q Learning;
D O I：
10.4108/ew.5047
中图分类号：
学科分类号：
摘要：
The study addresses the optimization of land transportation in the context of vehicle routing, a critical aspect of transportation logistics. The specific objectives are to employ various meta-heuristic optimization techniques, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), and Q-Learning reinforcement algorithm, to find the optimal solutions for vehicle routing problems. The primary aim is to enhance the efficiency and effectiveness of land transportation systems by minimizing factors such as travel distance or time while adhering to constraints. The study evaluates the advantages and limitations of each algorithm and introduces a novel-based approach that integrates Q-learning with the FA. The results demonstrate that these meta-heuristic optimization techniques offer promising solutions for complex vehicle routing challenges. The integrated Q-learning with Firefly Algorithm (iQLFA) emerges as the most successful approach among them, showcasing its potential to significantly improve transportation optimization outcomes. Copyright © 2024 K. R. Pratiba et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
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页码：1 / 6
页数：5
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