Tabu search guided by reinforcement learning for the max-mean dispersion problem

Nijimbere, Dieudonné; Zhao, Shengchuan; Gu, Xunhao; Esangbedo, Moses Olabhele; Dominique, Nyiribakwe

doi:10.3934/jimo.2020115

Cited by 9 publications

(3 citation statements)

References 28 publications

(74 reference statements)

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“…This is done by assigning rewards to the parameter change in the algorithm, storing them in a matrix, and learning the best values to apply to each parameter at runtime. In [47], another combination of a metaheuristic algorithm with reinforcement learning techniques is proposed. In this case, the tabu search was integrated with Q-Learning to find promising regions in the search space when the algorithm is stuck in a local optimum.…”

Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Preprint

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Stagnation at local optima represents a significant challenge in bio–inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca Predator Algorithm with Deep Q–Learning. Orca Predator Algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q–Learning is a reinforcement learning technique that combines Q–Learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of Orca Predator Algorithm to identify promising regions nearby in the search space, complemented by the fine–tuning capabilities of Deep Q–Learning to navigate these areas precisely. The practical application of this approach is evaluated using the high–dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of Orca Predator Algorithm against its native version and a set of state–of–the–art algorithms.

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Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This integration has led to significant advancements across several research domains. Innovations in local search optimization [32], dynamic parameter tuning [33], and the identification of promising search areas represent key areas of progress [34]. Studies have demonstrated the efficacy of this integration in enhancing algorithmic intelligence and adaptability, contributing to fields ranging from optimization challenges in theoretical contexts to practical applications like neural network training and cloud computing load balancing [35,36].…”

Section: Related Workmentioning

confidence: 99%

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Olivares,

Ravelo,

Soto

et al. 2024

Mathematics

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Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms.

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“…It is carried out by assigning rewards to the change of parameters in the algorithm, storing them in an array, and learning the best values to apply to each parameter at runtime. In [46], another combination of a metaheuristic algorithm with reinforcement learning techniques is proposed. In this case, tabu search was integrated with Q-Learning to find promising regions in the search space when the algorithm is stuck at a local optimum.…”

Section: Related Workmentioning

confidence: 99%

A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

Olivares,

Soto,

Crawford

et al. 2023

Axioms

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This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm’s parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP-hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm.

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Tabu search guided by reinforcement learning for the max-mean dispersion problem

Cited by 9 publications

References 28 publications

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection

A Learning—Based Particle Swarm Optimizer for Solving Mathematical Combinatorial Problems

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