Solving the Multiple choice Multidimensional Knapsack problem with ABC algorithm

Mkaouar, Arij; Htiouech, Skander; Chabchoub, Habib

doi:10.1109/cec48606.2020.9185872

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…Hifi et al [12] proposed a reactive local search algorithm for the multidimensional MCKP. Mkaouar et al [26] introduced a method based on the artificial bee colony algorithm to solve the multidimensional MCKP. In general, existing researches on MCKP cannot be applied, since the solutions in DDCCMCKP can not be evaluated by deterministic approaches.…”

Section: A Algorithms For Mckpmentioning

confidence: 99%

A Data-Driven Joint Chance-Constrained Game for Renewable Energy Aggregators in the Local Market

Chen

et al. 2023

IEEE Trans. Smart Grid

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Section: A Algorithms For Mckpmentioning

confidence: 99%

A Data-Driven Joint Chance-Constrained Game for Renewable Energy Aggregators in the Local Market

Chen

et al. 2023

IEEE Trans. Smart Grid

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“…The authors demonstrated the ability to convert a nondeterministic MMKP into an integer linear program without extra complexity. Mkaouar et al [70] developed an algorithm that uses the ABC algorithm to resolve the MMKP. Their proposed algorithm, inspired by the general behavior of the honeybee swarm, provided better quality solutions for medium and large scale instances compared to other reported approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To the best of our knowledge, the ABC algorithm has not yet been used for the MMKP, except in the study by Mkaouar et al [70] that presents an algorithm inspired by the general behavior of a honeybee swarm. However, this study represents the different phases of the ABC algorithm for the MMKP.…”

Section: Mabcmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

2023

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The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified Artificial Bee Colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several stateof-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS 2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems.INDEX TERMS Artificial bee colony algorithm, multiple-choice multidimensional knapsack problem, hamming distance, surrogate relaxation.

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“…Then, the new optimization problem P2 is mapped to a multidimensional multi-choice knapsack problem (MMKP) [31]. As P2 is NP-hard, P1 is a generalization of the MMKP, which is also NP-hard.…”

Section: Problem Formulationmentioning

confidence: 99%

Distributed Slice Selection-Based Computation Offloading for Intelligent Vehicular Networks

Tang

Duan

Zhou

et al. 2021

IEEE Open J. Veh. Technol.

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Distributed artificial intelligence (AI) is becoming an efficient approach to fulfill the high and diverse requirements for future vehicular networks. However, distributed intelligence tasks generated by vehicles often require diverse resources. A customized resource provision scheme is required to improve the utilization of multi-dimensional resources. In this work, a slice selection-based online offloading (SSOO) algorithm is proposed for distributed intelligence in future vehicular networks. First, the response time and energy consumption are reduced for processing tasks locally on the vehicles. Then, the offloading overheads, including latency and energy consumption, are calculated by considering the available resource amount, wireless channel states and vehicle conditions. The slice selection results is obtained by the deep reinforcement learning (DRL)-based method. Based on the selection solution, resource allocation results are achieved by KKT conditions and bisection method. Finally, the experimental results depict that the proposed SSOO algorithm outperforms other comparing algorithms in terms of energy consumption and task completion rate.

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Solving the Multiple choice Multidimensional Knapsack problem with ABC algorithm

Cited by 10 publications

References 25 publications

A Data-Driven Joint Chance-Constrained Game for Renewable Energy Aggregators in the Local Market

A Data-Driven Joint Chance-Constrained Game for Renewable Energy Aggregators in the Local Market

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

Distributed Slice Selection-Based Computation Offloading for Intelligent Vehicular Networks

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