Population based ant colony optimization on FPGA

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

doi:10.1109/fpt.2002.1188673

Cited by 10 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For symmetric instances, this number is halved, ⌈ n(n−1) 2 ρ⌉, since d j (x, y) = d j (y, x). Besides, the authors in [23] also suggest using the city replacement to generate dynamism into static TSPs [36], where a number of cities are replaced with new ones. Formally, given a graph G = (V, E) with v nodes and e edges, this benchmark generator replaces, every τ iterations, ⌈ρ × n 2 ⌉ random nodes (cities) in N in in the instance with the same number of random nodes in N out , where V is a set of nodes (cities), E is a set of arcs fully connecting the nodes, τ and ρ are the frequency and magnitude of changes, respectively, n is the size of the problem and N in ∩ N out = N are two sets containing n 2 nodes each.…”

Section: Dynamic Benchmark Generatorsmentioning

confidence: 99%

On the elusivity of dynamic optimisation problems

Alza

Bartlett

Ceberio

et al. 2023

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Section: Dynamic Benchmark Generatorsmentioning

confidence: 99%

On the elusivity of dynamic optimisation problems

Alza

Bartlett

Ceberio

et al. 2023

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

“…Whenever a dynamic change occurs, the solutions represented by the current ants in the population list may become invalid (e.g., when existing nodes are removed and replaced with new nodes). Therefore, these solutions are repaired heuristically using the KeepElite principle [33]. Specifically, the affected nodes are removed from the solution, reconnecting the successor and predecessor nodes.…”

Section: Adapting In Dynamic Environmentsmentioning

confidence: 99%

Measuring the Performance of Ant Colony Optimization Algorithms for the Dynamic Traveling Salesman Problem

Mavrovouniotis,

Anastasiadou,

Hadjimitsis

2023

Algorithms

View full text Add to dashboard Cite

Ant colony optimization (ACO) has proven its adaptation capabilities on optimization problems with dynamic environments. In this work, the dynamic traveling salesman problem (DTSP) is used as the base problem to generate dynamic test cases. Two types of dynamic changes for the DTSP are considered: (1) node changes and (2) weight changes. In the experiments, ACO algorithms are systematically compared in different DTSP test cases. Statistical tests are performed using the arithmetic mean and standard deviation of ACO algorithms, which is the standard method of comparing ACO algorithms. To complement the comparisons, the quantiles of the distribution are also used to measure the peak-, average-, and bad-case performance of ACO algorithms. The experimental results demonstrate some advantages of using quantiles for evaluating the performance of ACO algorithms in some DTSP test cases.

show abstract

“…Guntsch et al [34] is the earliest paper in our survey. 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.…”

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

View full text Add to dashboard Cite

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.

show abstract

Population based ant colony optimization on FPGA

Abstract: We propose to modify a type of ant algorithm called Population based Ant Colony Optimization (P-ACO)

Cited by 10 publications

References 10 publications

On the elusivity of dynamic optimisation problems

On the elusivity of dynamic optimisation problems

Measuring the Performance of Ant Colony Optimization Algorithms for the Dynamic Traveling Salesman Problem

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

Contact Info

Product

Resources

About