A Deep Averaged Reinforcement Learning Approach for the Traveling Salesman Problem

Parasteh, Sirvan; Khorram, Amin; Mouhoub, Malek; Sadaoui, Samira

doi:10.1109/smc53654.2022.9945274

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…To overcome these challenges, researchers have been exploring more innovative and efficient solutions. In recent years, reinforcement learning [10][11][12] and genetic algorithms [13,14] have emerged as two distinct optimization methods that have shown excellent performance in solving combinatorial optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Combining reinforcement learning algorithm and genetic algorithm to solve the traveling salesman problem

Ruan,

Cai,

Wang

2024

The Journal of Engineering

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With the growing recognition of the unique advantages of reinforcement learning and genetic algorithms in addressing combinatorial optimization problems, this study aims to integrate these two methods to collectively tackle the classic combinatorial optimization challenge of the travelling salesman problem (TSP). The TSP stands as a quintessential combinatorial optimization challenge, tasked with determining the shortest path among designated cities. This paper introduces an innovative approach by amalgamating reinforcement learning's path selection prowess with genetic algorithms' global search strategy, aiming to uncover superior solutions in TSP. Specifically, the experiment employs a dual Q‐learning algorithm within reinforcement learning to identify multiple optimal paths, serving as progenitors for the genetic algorithm to further enhance performance. The paper meticulously outlines the problem modelling process, elucidating TSP instance definitions, descriptions, and precise objective function definitions. Experimental findings underscore the substantial enhancements achievable in TSP optimization through this comprehensive approach, offering a fresh perspective and methodology for tackling combinatorial optimization challenges.

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Section: Introductionmentioning

confidence: 99%

Combining reinforcement learning algorithm and genetic algorithm to solve the traveling salesman problem

Ruan,

Cai,

Wang

2024

The Journal of Engineering

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“…Some of the most commonly used ones include Simulated Annealing (SA) [12], Tabu Search (TS) [3], Genetic Algorithm (GA) [4], and Ant Colony Optimization (ACO) [5]. In recent years, reinforcement learning has also emerged as a promising approach for tackling TSP problems [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the traveling salesman problem: Performance comparison of three algorithms

Wang,

Xiao,

Wang

et al. 2023

The Journal of Engineering

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Travelling salesman problem (TSP) is one of the most famous problems in graph theory, as well as one of the typical nondeterministic polynomial time (NP)‐hard problems in combinatorial optimization. Reinforcement learning (RL) has been widely regarded as an effective tool for solving combinatorial optimization problems. This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL algorithms (Q‐Learning, SARSA, and Double Q‐Learning) under different reward functions, ε‐greedy decay strategies, and running times. A comprehensive analysis and comparison of the three algorithms mentioned above were conducted in the experiment. First, the experimental results indicate that the Double Q‐Learning algorithm is the best algorithm. Among the eight TSP instances, the Double Q‐Learning algorithm outperforms the other two algorithms in five instances. Additionally, it has shorter runtimes compared to the SARSA algorithm and similar runtimes to the Q‐Learning algorithm across all instances. Second, upon analysing the results, it was found that using the reward strategy contributes to obtaining the best results for all algorithms. Among the 24 combinations of 3 algorithms and 8 instances, 17 combinations achieved the best results when the reward strategy was set to .

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Multi-UAV Weed Spraying

Farid,

Mouhoub,

Arkles

et al. 2024

Communications in Computer and Information Science

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A Deep Averaged Reinforcement Learning Approach for the Traveling Salesman Problem

Cited by 5 publications

References 16 publications

Combining reinforcement learning algorithm and genetic algorithm to solve the traveling salesman problem

Combining reinforcement learning algorithm and genetic algorithm to solve the traveling salesman problem

Reinforcement learning for the traveling salesman problem: Performance comparison of three algorithms

Multi-UAV Weed Spraying

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