Combinatorial Artificial Bee Colony Optimization with Reinforcement Learning Updating for Travelling Salesman Problem

Fairee, Suthida; Khompatraporn, Charoenchai; Prom-on, Santitham; Sirinaovakul, Booncharoen

doi:10.1109/ecti-con47248.2019.8955176

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…K-means clustering was either used to add diversity to the population or to detect the clusters in the optima [18], [31], [32]. Reinforcement learning has also been used in several studies to improve the searching process of the ABC [33], [34]. This method is mostly used in the onlooker and employed phases of the ABC algorithm.…”

Section: Studies Employing Learning Techniques On Abcmentioning

confidence: 99%

OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

Zahedi,

Mohammadi,

Amini

2021

Preprint

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Hyperparameter tuning in machine learning algorithms is a computationally challenging task due to the large-scale nature of the problem. In order to develop an efficient strategy for hyper-parameter tuning, one promising solution is to use swarm intelligence algorithms. Artificial Bee Colony (ABC) optimization lends itself as a promising and efficient optimization algorithm for this purpose. However, in some cases, ABC can suffer from a slow convergence rate or execution time due to the poor initial population of solutions and expensive objective functions. To address these concerns, a novel algorithm, OptABC, is proposed to help ABC algorithm in faster convergence toward a near-optimum solution. OptABC integrates artificial bee colony algorithm, K-Means clustering, greedy algorithm, and opposition-based learning strategy for tuning the hyper-parameters of different machine learning models. OptABC employs these techniques in an attempt to diversify the initial population, and hence enhance the convergence ability without significantly decreasing the accuracy. In order to validate the performance of the proposed method, we compare the results with previous state-of-the-art approaches. Experimental results demonstrate the effectiveness of the OptABC compared to existing approaches in the literature.

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Section: Studies Employing Learning Techniques On Abcmentioning

confidence: 99%

OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

Zahedi,

Mohammadi,

Amini

2021

Preprint

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“…are optimized. In today's complex and varied production processes, dynamic events such as machine breakdown or the change of the processing time and machine order of jobs are inevitable to be considered, necessitating the remarkable results on various combinatorial optimization problems such as traveling salesman problem (TSP) [12], the vehicle routing problem (VRP) [13] and JSP [14]. Existing research has shown that using reinforcement learning to solve DJSP has at least four advantages: 1) RL doesn't require the complete mathematical model or the large labeled datasets of the scheduling environment, but can learn from the interaction with the environment and store the learned knowledge to achieve "offine learning and online application" [15].…”

Section: Introductionmentioning

confidence: 99%

You Only Train Once: A highly generalizable reinforcement learning method for dynamic job shop scheduling problem

Zeng¹,

Liao²,

Li³

et al. 2022

Preprint

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<p>Research in artificial intelligence demonstrates the applicability and flexibility of the reinforcement learning (RL) technique for the dynamic job shop scheduling problem (DJSP). However, the RL-based method will always overfit to the training environment and cannot generalize well to novel unseen situations at deployment time, which is unacceptable in real-world production. For this reason, this paper proposes a highly generalizable reinforcement learning framework named Train Once For All (TOFA) for the dynamic job shop scheduling problem. The trivial and non-trivial states are distinguished when the DJSP is formulated as a semi-Markov decision process, defining the size-agnostic state, action, and reward function. A novel graph representation learning method based on attention mechanism and spatial pyramid pooling is implemented to compress the disjunctive graphs of differentsize DJSP into fixed-length feature vectors. Combining the proposed dynamic frame skipping and an improved prioritized experience replay method that considers the sample quality difference at different training phases. TOFA shows superb generalization capability, outperforms practically favored dispatching rules and even instance-by-instance training RL-based schedulers on various benchmark DJSP. Additionally, we proved that TOFA acquires a transferable scheduling policy that can be used to schedule a whole new DJSP without additional training.</p>

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Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search

Venske,

de Almeida,

Delgado

2024

J Heuristics

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Combinatorial Artificial Bee Colony Optimization with Reinforcement Learning Updating for Travelling Salesman Problem

Cited by 9 publications

References 7 publications

OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

OptABC: an Optimal Hyperparameter Tuning Approach for Machine Learning Algorithms

You Only Train Once: A highly generalizable reinforcement learning method for dynamic job shop scheduling problem

Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search

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