FPGA implementation of population-based ant colony optimization

Scheuermann, Bernd; So, Keith; Guntsch, Michael; Middendorf, Martin; Diessel, Oliver; ElGindy, Hossam A.; Schmeck, Hartmut

doi:10.1016/s1568-4946(04)00049-3

Cited by 8 publications

(10 citation statements)

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“…It appears in 2002 and proposes a design for an Ant Colony Optimization (ACO) variant, called Populationbased ACO (P-ACO), that allows efficient FPGA implementation. In [81], an overlapping set of authors report from the actual implementation of the P-ACO design. They conduct experiments on random instances of the Single Machine Total Tardiness Problem (SMTTP) with number of jobs ranging from 40 to 320 and report moderate speedups between 1.6 and 10 relative to a software implementation.…”

Section: Early Work On Non-gpu Related Acceleratorsmentioning

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

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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: Early Work On Non-gpu Related Acceleratorsmentioning

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

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“…Scheuermann et al [21,22] designed parallel implementations of ACO on Field Programmable Gate Arrays (FPGA). Considerable changes to the algorithmic structure of the metaheuristic were needed to take benefit of this particular architecture.…”

Section: Hardware-oriented Parallel Acomentioning

confidence: 99%

Parallel Ant Colony Optimization: Algorithmic Models and Hardware Implementations

Delisle¹

2013

Ant Colony Optimization - Techniques and Applications

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“…These implementations have utilized inherent parallel capabilities of the hardware, such as hierarchical memory model in General Purpose Graphics Processing Units (GPGPU) [13], [21], redundant processor connections in Optical Pipelined Reconfigurable Mesh (PR-Mesh) systems [22], runtime reconfigurable processor arrays [23], and FPGA [24]. A number of papers have also proposed ACO implementations for the MapReduce framework, a programming model and software framework for distributed data processing [25], [26] …”

Section: Related Workmentioning

confidence: 99%

Fine-grained Parallel Ant Colony System for Shared-Memory Architectures

Hadian¹,

Shahrivari²,

Minaei-bidgoli³

2012

IJCA

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Although Ant Colony Systems (ACS) have gained much attention in last two decades but slow execution and convergence speed are still two challenges for these metaheuristic algorithms. Many parallel implementations have been proposed for faster execution. However, most of available implementations use coarse-grained synchronization mechanisms that are not efficient and scalable. In this work, we have taken a fine-grained (ant-level) approach that is more efficient and scalable. We have used traveling salesman problem as a test case and have presented a parallel finegrained implementation for shared-memory multi-core systems. Our experimental results show that our proposed parallel implementation can achieve considerably higher speedup values on modern multicore processors.

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FPGA implementation of population-based ant colony optimization

Cited by 8 publications

References 0 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

Parallel Ant Colony Optimization: Algorithmic Models and Hardware Implementations

Fine-grained Parallel Ant Colony System for Shared-Memory Architectures

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