Using surrogate information to solve the multidimensional multi-choice knapsack problem

Htiouech, Skander; Bouamama, Sadok; Attia, Rabeh

doi:10.1109/cec.2013.6557817

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…For large instances (I07 − I13), our approaches give the best results in a very short time (< 10s). AMMKP provides solutions with total value on average equal to 0.81% close to the optimum and better performance than those obtained by Moser [4]( 3.62%), Iqbal et al [21](1.58% ), Htiouech et al [13](1.03% ) and Xia et al (1.26% ). For instance I13 (which is a very large instance of MMKP; 4000 variables), note that our approach takes more lower computational time to reach its result which is 0.69% of the optimal solution (given by CPLEX).…”

Section: Experimental Designmentioning

confidence: 65%

“…The patterns are, respectively, based on a local branching algorithm, on a hybrid algorithm combining local branching with column generation, and on a truncated branch-and-bound algorithm that embeds the previous hybrid method. Htiouech et al [13] describe an oscillation heuristic which explores both sides of the feasibility border; it uses surrogate constraint information for building the choice rules. In order to strengthen their constraint information, the authors enhance the method with constraints normalization.…”

Section: Related Workmentioning

confidence: 99%

“…The procedure proposed by Htiouech et al [13] use surrogate constraint information as choice rules to intensify the research. The authors use a normalization phase to strengthen the surrogate constraint information in order to improve the computational results.…”

Section: Behavior Of Agentsmentioning

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%

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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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Section: Experimental Designmentioning

confidence: 65%

Section: Related Workmentioning

confidence: 99%

Section: Behavior Of Agentsmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

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Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Htiouech¹,

Alzaidi²

2017

IJCA

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“…Then he developed the surrogate relaxation for this obtained problem. This methodology is applied to resolve multi-attribute project portfolio selection [57]. proposed a new heuristic for solving 0/1 MCMKP, it isbased on oscillation strategy that explores both sides of the feasibility border, by using surrogate constraint information as choice rules.…”

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

Xia

Gao

2015

Communications in Computer and Information Science

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Using surrogate information to solve the multidimensional multi-choice knapsack problem

Cited by 9 publications

References 32 publications

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Smart Agents for the Multidimensional Multi-choice Knapsack Problem

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

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

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