Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem

Arin, Arif; Rabadi, Ghaith

doi:10.1016/j.cie.2016.10.022

Cited by 18 publications

(12 citation statements)

References 132 publications

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“…Estimation of distribution algorithms (EDA) are a class of population-based algorithms [16][17][18][19][20], which have been demonstrated to be effective for solving many optimization problems. One of the most distinct features of EDA lies in the use of global statistical information extracted from a set of high-fitness solutions to build a probability model.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Randomly choose a variable from (14) else (15) Randomly choose a variable from (16) end if (17) end if (18) Assign the chosen variable to be value 1 to enlarge the current solution (19) phase. When creating a new starting solution, the first added variable is determined according to the preference probability information.…”

Section: Eda Guided Solution Construction Phase Algorithmmentioning

confidence: 99%

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A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max‐Mean Dispersion Problem

Nijimbere

Zhao

Liu

et al. 2019

Mathematical Problems in Engineering

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This paper presents a hybrid metaheuristic that combines estimation of distribution algorithm with tabu search (EDA-TS) for solving the max-mean dispersion problem. The proposed EDA-TS algorithm essentially alternates between an EDA procedure for search diversification and a tabu search procedure for search intensification. The designed EDA procedure maintains an elite set of high quality solutions, based on which a conditional preference probability model is built for generating new diversified solutions. The tabu search procedure uses a fast 1-flip move operator for solution improvement. Experimental results on benchmark instances with variables ranging from 500 to 5000 disclose that our EDA-TS algorithm competes favorably with state-of-the-art algorithms in the literature. Additional analysis on the parameter sensitivity and the merit of the EDA procedure as well as the search balance between intensification and diversification sheds light on the effectiveness of the algorithm.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Eda Guided Solution Construction Phase Algorithmmentioning

confidence: 99%

A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max‐Mean Dispersion Problem

Nijimbere

Zhao

Liu

et al. 2019

Mathematical Problems in Engineering

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“…The proposed algorithm is tested using the 0/1 MKP and provides good results especially for large scale instances in less time compared to other state-of-the-art algorithms. That is, [159] propose the integration of two other methods into Meta-Raps: EDA and a machine learning algorithm known as Q-Learning. The two proposed algorithms are tested on MKP Benchmark data.…”

Section: Other Metaheuristic Approachesmentioning

confidence: 99%

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Laabadi¹,

Naimi²,

Amri³

et al. 2018

AJOR

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The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

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“…Reinforcement learning (RL) is an artificial intelligence technique with relevant applications in robotics [8,15,[28][29][30]37], path planning [20,39,47,59,75,76] and combinatorial optimization problems [4,7,13,14,21,44,53,54,64,79], such as the TSP [1,2,18,22,41,45,52,66,81]. In RL, an agent learns from rewards and penalties in interacting with an environment [68].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the traveling salesman problem with refueling

Ottoni

Nepomuceno

Oliveira

et al. 2021

Complex Intell. Syst.

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The traveling salesman problem (TSP) is one of the best-known combinatorial optimization problems. Many methods derived from TSP have been applied to study autonomous vehicle route planning with fuel constraints. Nevertheless, less attention has been paid to reinforcement learning (RL) as a potential method to solve refueling problems. This paper employs RL to solve the traveling salesman problem With refueling (TSPWR). The technique proposes a model (actions, states, reinforcements) and RL-TSPWR algorithm. Focus is given on the analysis of RL parameters and on the refueling influence in route learning optimization of fuel cost. Two RL algorithms: Q-learning and SARSA are compared. In addition, RL parameter estimation is performed by Response Surface Methodology, Analysis of Variance and Tukey Test. The proposed method achieves the best solution in 15 out of 16 case studies.

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Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem

Cited by 18 publications

References 132 publications

A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max‐Mean Dispersion Problem

A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max‐Mean Dispersion Problem

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Reinforcement learning for the traveling salesman problem with refueling

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