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

Quan, Zhe; Wu, Lei

doi:10.1002/cpe.3848

Cited by 9 publications

(11 citation statements)

References 26 publications

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“…In 2017, Salem et al [26] designed a probabilistic tabu search algorithm (PTS) that operates with multiple neighborhoods. In the same year, Quan and Wu investigated two parallel algorithms: the parallel neighborhood search algorithm (PNS) [25] and the cooperative parallel adaptive neighborhood search algorithm (CPANS) [24]. They also designed a new set of 50 DCKP large instances with 1500 and 2000 items (see Section 4.1).…”

Section: Related Workmentioning

confidence: 99%

A threshold search based memetic algorithm for the disjunctively constrained knapsack problem

Wei

Hao

2021

Preprint

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The disjunctively constrained knapsack problem consists in packing a subset of pairwisely compatible items in a capacity-constrained knapsack such that the total profit of the selected items is maximized while satisfying the knapsack capacity. DCKP has numerous applications and is however computationally challenging (NP-hard). In this work, we present a threshold search based memetic algorithm for solving the DCKP that combines the memetic framework with threshold search to find high quality solutions. Extensive computational assessments on two sets of 6340 benchmark instances in the literature demonstrate that the proposed algorithm is highly competitive compared to the state-of-the-art methods. In particular, we report 24 and 354 improved best-known results (new lower bounds) for Set I (100 instances) and for Set II (6240 instances), respectively. We analyze the key algorithmic components and shed lights on their roles for the performance of the algorithm. The code of our algorithm will be made publicly available.

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Section: Related Workmentioning

confidence: 99%

A threshold search based memetic algorithm for the disjunctively constrained knapsack problem

Wei

Hao

2021

Preprint

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“…Tabu search [Rudek, 2014, Jin et al, 2012, Bozejko et al, 2017, Bozejko et al, 2013, Czapinski and Barnes, 2011, James et al, 2009, Czapiński, 2013, Bukata et al, 2015, Cordeau and Maischberger, 2012, Wei et al, 2017, Janiak et al, 2008, Shylo et al, 2011, Jin et al, 2014, Bożejko et al, 2016, Jin et al, 2011, Maischberger and Cordeau, 2011, Van Luong et al, 2013, Dai et al, 2009] Simulated annealing [Thiruvady et al, 2016, Rudek, 2014, Defersha, 2015, Mu et al, 2016, Ferreiro et al, 2013, Lou and Reinitz, 2016, Banos et al, 2016, 2016, Lazarova and Borovska, 2008 Variable neigborhood search [Yazdani et al, 2010, Lei and Guo, 2015, Davidović and Crainic, 2012, Quan and Wu, 2017, Menendez et al, 2017, Eskandarpour et al, 2013, Coelho et al, 2016, Polat, 2017, Tu et al, 2017, Aydin and Sevkli, 2008, Polacek et al, 2008 (Greedy randomized)…”

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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“…Quan and Wu 29 presented a design and evaluation of a parallel neighbor algorithm for the disjunctively constrained knapsack problem. They proposed a parallel multi-neighborhood search method that simulates a teamwork that can find more feasible solutions gradually in the given timesteps.…”

Section: Related Workmentioning

confidence: 99%

GPU‐based branch‐and‐bound method to solve large 0‐1 knapsack problems with data‐centric strategies

Shen

Shigeoka

Ino

et al. 2018

Concurrency and Computation

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An out-of-core branch-and-bound (B&B) method to solve large 0-1 knapsack problems on a graphics processing unit (GPU) is proposed. Given a large problem that produces many subproblems, the proposed method dynamically swaps subproblems to CPU memory. Because such a CPU-centric subproblem management scheme increases CPU-GPU data transfer, we adopt three data-centric strategies to eliminate this side effect. The first is an out-of-order search (O3S) strategy that reduces the data transfer overhead by adaptively transferring subproblems between the CPU and GPU. The second is an explicitly-managed pipelining strategy that hides the data transfer overhead by overlapping data transfer with GPU-based B&B operations. The third is a GPU-based stream compaction strategy that reduces the sparseness of arrays to be transferred. Experimental results demonstrate that the proposed out-of-core method stored 41 times as many subproblems as a previous in-core method that manages subproblems in GPU memory, solving approximately twice as many problem instances on the GPU. In addition, compared to a previous breadth-first search (BFS) strategy, the proposed O3S strategy achieved an average speedup of 7.5 times. KEYWORDSbranch and bound, data-centric method, GPU, knapsack, out-of-core computation Concurrency Computat Pract Exper. 2019;31:e4954.wileyonlinelibrary.com/journal/cpe FIGURE 3 Array-based subproblem management scheme. Pruned subproblems (ie, passive; X) make the array sparse as the depth of the search tree increases. Note that the branching, bounding, and pruning operations are performed on the GPU SUPPORTING INFORMATIONAdditional supporting information may be found online in the Supporting Information section at the end of the article.How to cite this article: Shen J, Shigeoka K, Ino F, Hagihara K. GPU-based branch-and-bound method to solve large 0-1 knapsack problems with data-centric strategies. Concurrency Computat Pract Exper. 2019;31:e4954. https://doi.

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

Cited by 9 publications

References 26 publications

A threshold search based memetic algorithm for the disjunctively constrained knapsack problem

A threshold search based memetic algorithm for the disjunctively constrained knapsack problem

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

GPU‐based branch‐and‐bound method to solve large 0‐1 knapsack problems with data‐centric strategies

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