A parallel immune algorithm for traveling salesman problem and its application on cold rolling scheduling

Zhao, Jun; Liu, Quanli; Wang, Wei; Wei, Zhuoqun; Shi, Peng

doi:10.1016/j.ins.2010.12.003

Cited by 34 publications

(25 citation statements)

References 18 publications

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“…Year Problem Algorithm Operators Li et al [53] 2009 TSP IEA PMX, mutation Chen et al [17] 2011 TSP GA crossover, 2-opt mutation Fujimoto and Tsutsui [32] 2011 TSP GA OX, 2-opt local search gene string move, Zhao et al [98] 2011 TSP IEA multi bit exchange Author Selection Immune next Population Li et al [53] Better Tournament Chen et al [17] Best Fujimoto and Tsutsui [32] Best Zhao et al [98] Best position Tournament Author GPU(s) Max. Speedup CPU code Li et al [53] GeForce 9600 GT 11.5 ?…”

Section: Authormentioning

confidence: 99%

“…Fujimoto and Tsutsui [32] GeForce GTX 285 24.2 ? Zhao et al [98] GeForce GTS 250 7.5 ? Table 2: Overview of EA GPU implementations on the GPU for routing.…”

Section: Authormentioning

confidence: 99%

“…Crossover Mutation Vaccination Tour evaluation T B T B T B T B Li et al [53] x Chen et al [17] x x Fujimoto and Tsutsui [32] x x x Zhao et al [98] x U Table 3: Studied implementation approaches with respect to whether one individual is assigned to one thread or block. Legend: T -thread; B -block; Uuncertain.…”

Section: Authormentioning

confidence: 99%

See 2 more Smart Citations

GPU computing in discrete optimization. Part II: Survey focused on routing problems

Schulz

Hasle

Brodtkorb

et al. 2013

EURO Journal on Transportation and Logistics

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In many cases there is still a large gap between the performance of current optimization technology and the requirements of real world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs include a multi-core CPU and at least one GPU, providing a low cost, easily accessible heterogeneous environment for high performance computing. New solution methods that combine task parallelization and stream processing are needed to fully exploit modern computer architectures and profit from future hardware developments. This paper is the second in a series of two. Part I gives a tutorial style introduction to modern PC architectures and GPU programming. Part II gives a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems. We assume that the reader is familiar with GPU programming, and refer the interested reader to Part I. We conclude with lessons learnt, directions for future research, and prospects.

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Section: Authormentioning

confidence: 99%

“…Fujimoto and Tsutsui [32] GeForce GTX 285 24.2 ? Zhao et al [98] GeForce GTS 250 7.5 ? Table 2: Overview of EA GPU implementations on the GPU for routing.…”

Section: Authormentioning

confidence: 99%

See 1 more Smart Citation

GPU computing in discrete optimization. Part II: Survey focused on routing problems

Schulz

Hasle

Brodtkorb

et al. 2013

EURO Journal on Transportation and Logistics

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“…Until now there have been just a few articles that investigated the possibilities of using the GPU graphic card in solving the problems defined on a graph such as TSP [14,15]. Prior works on GPU-based TSP solver were based on some variants of Ant Colony Optimization (ACO) [16] which has been inspired by ant foraging behaviour that results in finding the shortest path between their nest and the food source.…”

Section: Heuristics For Solving Tspmentioning

confidence: 99%

“…The focus of most of them is on the comparison of GPU and CPU implementation and they do not address how often their implementation reaches the optimal TSP solutions. To the best of our knowledge, there have been a few other articles that investigated the possibilities of using the GPU graphic card in solving TSP such as the evolutionary algorithm [15] and immune algorithm [14]. Even the study that uses CUDA implementation of hill climbing [21] or the one in which a parallel local search is used [22] fail to run GPU implementation on TSPLIB problems which have more than 2,000 cities.…”

Section: Heuristics For Solving Tspmentioning

confidence: 99%

A GPU Implementation of Local Search Operators for Symmetric Travelling Salesman Problem

Fosin¹,

Davidović²,

Carić³

2013

PROMET

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Parallel iterative hill climbing algorithm to solve TSP on GPU

Yelmewad

Talawar

2018

Concurrency and Computation

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Summary Traveling Salesman Problem (TSP) is an NP‐hard combinatorial optimization problem. Heuristic algorithms provide satisfactory solutions to large instance TSP in a reasonable amount of time. However, heuristic methods result in suboptimal solutions as they do not cover the search space adequately. Sequential heuristic approaches spend significant CPU time in neighborhood generation for large input instances. Neighborhood generation time can be reduced by generating in parallel. GPUs have been shown to be effective in exploiting data and memory level parallelism in large complex problems. This work presents a GPU‐based Parallel Iterative Hill Climbing (PIHC) algorithm using the nearest neighborhood heuristic to arrive at near‐optimal solutions of large TSPLIB instances in a reasonable amount of time. Multiple construction heuristics approaches, thread mapping strategies, and data structures for TSPLIB instances have been evaluated. We demonstrate improved cost quality on symmetric TSPLIB instances up to 85,900 cities. The PIHC GPU implementation gives up to 193× speedup over its sequential counterpart and up to 979.96× speedup over a state‐of‐the‐art GPU‐based TSP solver. The PIHC implementation gives a cost quality with error rate 0.72% in the best case and 8.06% in the worst case.

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A parallel immune algorithm for traveling salesman problem and its application on cold rolling scheduling

Cited by 34 publications

References 18 publications

GPU computing in discrete optimization. Part II: Survey focused on routing problems

GPU computing in discrete optimization. Part II: Survey focused on routing problems

A GPU Implementation of Local Search Operators for Symmetric Travelling Salesman Problem

Parallel iterative hill climbing algorithm to solve TSP on GPU

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