Integrated Q-Learning with Firefly Algorithm for Transportation Problems

Q3 Engineering EAI Endorsed Transactions on Energy Web Pub Date : 2024-02-06 DOI:10.4108/ew.5047

K. R. Pratiba, S. Ridhanya, J. Ridhisha, P. Hemashree

引用次数: 0

Abstract

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.

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针对交通问题的 Q-Learning 与 Firefly 算法集成

这项研究针对陆路运输中的车辆路线优化问题，这是运输物流的一个重要方面。具体目标是采用各种元启发式优化技术，包括遗传算法 (GA)、蚁群优化 (ACO)、萤火虫算法 (FA)、粒子群优化 (PSO) 和 Q-Learning 强化算法，为车辆路线问题找到最佳解决方案。其主要目的是在遵守约束条件的同时，通过最大限度地减少旅行距离或时间等因素，提高陆路运输系统的效率和效益。研究评估了每种算法的优势和局限性，并引入了一种基于 Q-learning 与 FA 相结合的新方法。结果表明，这些元启发式优化技术为复杂的车辆路由挑战提供了有前途的解决方案。集成 Q-learning 与萤火虫算法（iQLFA）是其中最成功的方法，展示了其显著改善交通优化结果的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.