A Stochastic Local Search Heuristic for the Multidimensional Multiple-choice Knapsack Problem

Xia, Yifan; Gao, Chao; Li, Jinlong

doi:10.1007/978-3-662-49014-3_46

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Parra-Hernandez et al [18], Xia et al [19], and Shojaei et al [20] belong to this route. The second ones pay more efforts on high quality solutions.…”

Section: Accepted Manuscriptmentioning

confidence: 97%

An iterative pseudo-gap enumeration approach for the Multidimensional Multiple-choice Knapsack Problem

Gao

Yao

et al. 2017

European Journal of Operational Research

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“…Parra-Hernandez et al [18], Xia et al [19], and Shojaei et al [20] belong to this route. The second ones pay more efforts on high quality solutions.…”

Section: Accepted Manuscriptmentioning

confidence: 97%

An iterative pseudo-gap enumeration approach for the Multidimensional Multiple-choice Knapsack Problem

Gao

Yao

et al. 2017

European Journal of Operational Research

Self Cite

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“…Subsequently, Htiouech and Alzaidi [66] divided the MMKP into small subproblems and used an agent-based approach to solve the reduced problem. Xia et al [67] proposed a first-level tabu search algorithm. Their proposed algorithm performs fairly well compared with legacy heuristic approaches.…”

Section: Literature Reviewmentioning

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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“…We compare our heuristic to other methods from the literature. The set of 13 regular instances (named I01 − I13) due to Khan et al [6] are correlated instances and frequently used to test recent MMKP algorithms [40] [13] [41]. For each instance, we present in Table 1 the number of groups n, the number of items n i in each group i, the number of constraints m and the total number N of variables N = n i=1 n i .…”

Section: Experimental Designmentioning

confidence: 99%

“…Table 2 shows the performance results of different approaches including ours. To evaluate our algorithm, we compare the results (Table 2) with existing literature approaches for the MMKP, namely respectively, Moser [4] (Moser in 1997), Iqbal et al [21](Ant in 2010), Htiouech et al [13](OSC in 2013) and Xia et al [41](SLS in 2015). The values in bold indicate that our results are greater than or equal to the best results.…”

Section: Experimental Designmentioning

confidence: 99%

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Htiouech¹,

Alzaidi²

2017

IJCA

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In this paper, we propose a multi-agent approach for solving the multidimensional multi-choice knapsack problem (called MMKP). The MMKP is an NP-Hard optimization problem in strong sense. It is considered as a combination of two other variants such as: the multi-choice knapsack problem (MCKP) and the multidimensional knapsack problem (MDKP). The MMKP can be applied in many problems in real world. It can model many industrial situations, such as capital budgeting, model of allocation resources and finance. The particular properties of the MMKP favor its decomposition into many MMKP sub-problems with small sizes. The assignment of sub-problems and the sharing of available resources are allocated to a first agent. Each subproblem is then solved by an agent. To work collaboratively, a strategic negotiation between agents has been defined. A coordinator agent (CA) will evaluate and merge the generated solutions to build a feasible solution to the initial problem. The choice rules of the CA is modeled as a multidimensional knapsack problem (MKP). The proposed method is able to solve several instances of literature effectively, in particular for large size instances. General TermsMetaheuristic, knapsack problem

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A Stochastic Local Search Heuristic for the Multidimensional Multiple-choice Knapsack Problem

Cited by 7 publications

References 27 publications

An iterative pseudo-gap enumeration approach for the Multidimensional Multiple-choice Knapsack Problem

An iterative pseudo-gap enumeration approach for the Multidimensional Multiple-choice Knapsack Problem

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

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

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