A Fast Large Neighborhood Search for Disjunctively Constrained Knapsack Problems

Hifi, Mhand; Saleh, Sagvan; Wu, Lei

doi:10.1007/978-3-319-14115-2_34

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…For this, a random destroying strategy is proposed as a diversification procedure. This strategy tries to randomly explore the subsolution spaces to find the best local solution (Hifi et al, 2014).…”

Section: Third Stage: a Random Destroying Strategy To Diversify The S...mentioning

confidence: 99%

“…Steps 2-7 show the main steps in this algorithm. The idea is that, destroy the current solution obtained from stage two by removing 𝛼% of its items (see steps [3][4][5]. The destroyed solution (𝑆 𝑃𝐶𝐴𝐿𝐼𝑃 𝑑 ) then goes back to algorithm 1 to be completed and provide another feasible solution.…”

Section: Third Stage: a Random Destroying Strategy To Diversify The S...mentioning

confidence: 99%

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An Iterative Three-Stage Neighborhood Search for Solving Precedence Constrained Agricultural Land Investment Problem

Sarhan,

Saleh

2023

JMCIE

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The use of neighborhood search techniques to address a practical issue faced by agricultural investors is examined in this study. The problem is named as agricultural land investment problem with precedence constraints and it has an essential impact on agriculture issues. The tackled problem can be viewed as a variant of the well-known classical 0-1 knapsack problem where precedence constraints are imposed on pairs of items. Precedence constraints take into account a precedence relation between items. This paper first simulates the considered problem as precedence constraints knapsack problem and presents a mathematical representation model. Then, an iterative three-stage neighborhood search method is proposed for optimizing the problem. The proposed method consists of three stages. First stage applies a greedy procedure in order to construct a feasible solution. Second stage applies local search procedures in order to enhance the quality of the solutions at hand. Third and last, in order to broaden the search space, a random neighborhood destruction approach is introduced. Finally, the effectiveness of the suggested approach is assessed and contrasted with the outcomes obtained by greedy and local search techniques. The presented method is competitive and efficient since it produces excellent solutions in a reasonable amount of time.

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Section: Third Stage: a Random Destroying Strategy To Diversify The S...mentioning

confidence: 99%

Section: Third Stage: a Random Destroying Strategy To Diversify The S...mentioning

confidence: 99%

An Iterative Three-Stage Neighborhood Search for Solving Precedence Constrained Agricultural Land Investment Problem

Sarhan,

Saleh

2023

JMCIE

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“…First, we introduce an efficient heuristic to find a feasible solution for the DCKP, noted by H, which is also a suitable method for improving the performance of the neighborhood search ( [10]). The main conception of H is to deduce a feasible solution from an independent set.…”

Section: Finding a Feasible Solution For The Dckp: Hmentioning

confidence: 99%

“…In the context of metaheuristic, in last decade, reactive local search ( [6]), local branching ( [7]), scatter search ( [8]), iterative rounding search ( [9]), have shown a great interest in solving large-scale instances of the DCKP. Recently, Hifi et al [10,11] proposed a neighborhood search based heuristic for solving the DCKP, noted by NS. NS is based on randomly building and exploring a series of sub solution spaces to improve the current locally optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Design and evaluation of a parallel neighbor algorithm for the disjunctively constrained knapsack problem

Quan

2016

Concurrency and Computation

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Summary We investigate the use of a parallel computing model for solving the disjunctively constrained knapsack problem. This parallel approach is based on a multi‐neighborhood search. In this approach, search threads asynchronously exchange information about the best solutions and use the information to guide the search. The performance of the proposed method was evaluated on the set of the standard benchmark instances. We show encouraging results and compare them to the state‐of‐the‐art solutions. Copyright © 2016 John Wiley & Sons, Ltd.

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A 2-Approximation Algorithm for the Minimum Knapsack Problem With a Forcing Graph

Takazawa

Mizuno

2017

JORSJ

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Carnes and Shmoys [2] presented a 2-approximation algorithm for the minimum knapsack problem. We extend their algorithm to the minimum knapsack problem with a forcing graph (MKPFG), which has a forcing constraint for each edge in the graph. The forcing constraint means that at least one item (vertex) of the edge must be packed in the knapsack. The problem is strongly NP-hard, since it includes the vertex cover problem as a special case. Generalizing the proposed algorithm, we also present an approximation algorithm for the covering integer program with 0-1 variables.

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A Fast Large Neighborhood Search for Disjunctively Constrained Knapsack Problems

Cited by 3 publications

References 12 publications

An Iterative Three-Stage Neighborhood Search for Solving Precedence Constrained Agricultural Land Investment Problem

An Iterative Three-Stage Neighborhood Search for Solving Precedence Constrained Agricultural Land Investment Problem

Design and evaluation of a parallel neighbor algorithm for the disjunctively constrained knapsack problem

A 2-Approximation Algorithm for the Minimum Knapsack Problem With a Forcing Graph

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