A Parallel Large Neighborhood Search-Based Heuristic for the Disjunctively Constrained Knapsack Problem

Hifi, Mhand; Negre, Stephane; Saadi, Toufik; Saleh, Sagvan; Wu, Lei

doi:10.1109/ipdpsw.2014.173

Cited by 11 publications

(7 citation statements)

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“…PNS was coded in C++ using MPI library and all testes were conducted on a machine with Intel Xeon, 2×3.06 Ghz with 6‐Core. The obtained results were also compared with the best solutions from literature (cf., ). Outcomes for the 50 considered benchmark problems are shown in table , where B e s t corresponds to the best solutions from literature; V Cplex (resp.…”

Section: Computational Resultsmentioning

confidence: 97%

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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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Section: Computational Resultsmentioning

confidence: 97%

“…Parameters setting used to perform PNS: setting 1. . QUAN AND L. WU solutions from literature (cf.,[8][9][10]26]). Outcomes for the 50 considered benchmark problems are shown in table III, where Best corresponds to the best solutions from literature; V Cplex (resp.…”

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confidence: 99%

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

Quan

2016

Concurrency and Computation

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“…adaptive search [Caniou et al, 2012, Santos et al, 2010 Other single solution heuristics [Hifi et al, 2014] Population-based metaheuristics:…”

Section: Algorithm Typementioning

confidence: 99%

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Schryen

2020

European Journal of Operational Research

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Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

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“…The exact solution method is based on the principles of implicit enumeration search which starts its search from the initial solution obtained from the approximate part. Hifi et al (Hifi et al, 2014) proposed a parallel method based on the principles of large neighborhood search method for solving the disjunctively constrained knapsack problem. The method is designed using message passing interface.…”

Section: Related Workmentioning

confidence: 99%

A Parallel Heuristic Method for Optimizing a Real Life Problem (Agricultural Land Investment Problem)

Saleh

2018

ACAD J NAWROZ UNIV

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This paper proposed a parallel method for solving the Agricultural Land Investment Problem (ALIP), the problem that has an important impact on the agriculture issues. The author is first represent mathematically the problem by introducing a mathematical programming model. Then, a parallel method is proposed for optimizing the problem. The proposed method based on principles of parallel computing and neighborhood search methods. Neighborhood search techniques explore a series of solutions spaces with the aim of finding the best one. This is exploited in parallel computing, where several search processes are performed simultaneously. The parallel computing is designed using Message Passing Interface (MPI) which allows to build a flexible parallel program that can be executed in multicore and/or distributed environment. The method is competitive since it is able to solve a real life problem and yield high quality results in a fast solution runtime.

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A Parallel Large Neighborhood Search-Based Heuristic for the Disjunctively Constrained Knapsack Problem

Cited by 11 publications

References 9 publications

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

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

Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

A Parallel Heuristic Method for Optimizing a Real Life Problem (Agricultural Land Investment Problem)

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