A survey on parallel ant colony optimization

Pedemonte, Martín; Nesmachnow, Sergio; Cancela, Héctor

doi:10.1016/j.asoc.2011.05.042

Cited by 181 publications

(91 citation statements)

References 46 publications

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“…Here, several colonies independently explore the search space using their own pheromone matrices. The colonies can cooperate by periodically exchanging information [71]. On a single GPU this approach can be realized by assigning one colony per block, as done by Bai et al in [5] and by Delévacq et al in [25].…”

Section: Authormentioning

confidence: 99%

“…The two predominant, basic parallelization schemes are parallel ants, where one process/thread is allocated to each ant, and the multiple colonies approach. In [71], Pedemonte et al introduce a new taxonomy for classifying parallel ACO algorithms and also present a systematic survey of the current state-of-the-art on parallel ACO implementations. As part of the new taxonomy they describe the master-slave category, where a master process manages global information and slave processes perform subordinate tasks.…”

Section: Swarm Intelligence Metaheuristicsmentioning

confidence: 99%

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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%

Section: Swarm Intelligence Metaheuristicsmentioning

confidence: 99%

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

Schulz

Hasle

Brodtkorb

et al. 2013

EURO Journal on Transportation and Logistics

View full text Add to dashboard Cite

show abstract

“…Recently, a more detailed classification was proposed by Pedemonte et al [4]. It shows that most existing works are based on designing parallel ACO algorithms at a relatively high level of abstraction which may be suitable for conventional parallel computers.…”

Section: Introductionmentioning

confidence: 99%

Parallel Ant Colony Optimization: Algorithmic Models and Hardware Implementations

Delisle¹

2013

Ant Colony Optimization - Techniques and Applications

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“…In the last years, several works were devoted to the implementation of parallel ACO algorithms [4]. Most of these use clusters of PCs, where the workload is distributed to multiple computers [5].…”

Section: Introductionmentioning

confidence: 99%