Parallel ant colony for nonlinear function optimization with graphics hardware acceleration

Abstract

This paper presents a massively parallel Ant Colony Optimization -Pattern Search (ACO-PS) algorithm with graphics hardware acceleration on nonlinear function optimization problems. The objective of this study is to determine the effectiveness of using Graphics Processing Units (GPU) as a hardware platform for ACO-PS. GPU, the common graphics hardware found in modern personal computers, can be used for data-parallel computing in a desktop setting. In this research, the classical ACO is adapted in the data-parallel GPU computing platform featuring 'Single Instruction -Multiple Thread' (SIMT). The global optimal search of the ACO is enhanced by the classical local Pattern Search (PS) method. The hybrid ACO-PS method is implemented in a GPU+CPU hardware platform and compared to a similar implementation in a CentralProcessing Unit (CPU) platform. Computational results indicate that GPU-accelerated SIMT-ACO-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ACO-PS with GPU acceleration.

Parallel Ant Colony Optimization: Algorithmic Models and Hardware Implementations

“…Wang et al [24] propose an implementation of the MMAS where the tour construction phase is executed on a GPU to solve a 30 city TSP. Similar implementations are reported by You [25], Zhu and Curry [26], Li et al [27], Cecilia et al [28] and Delévacq et al [9] . Following these works, Delévacq et al [10] have proposed various parallelization strategies for ACO on GPU as well as a comparative study to show the influence of various parameters on search efficiency.…”

Parallelization of Intelligent Optimization Algorithm

“…The research in [19,20] studied how to implement optimization algorithms on GPU, while Kalivarapu [16] not only achieved the implementation of Particle Swarm Algorithm on computer clusters, but also on GPU. In addition, relied on GPU, Zhu and Curry [21] gave a detailed study of Ant Colony Algorithm and its parallel application, and Chitty [22], Li et al [23] carried on a specifically concrete work on the implementation of Genetic Algorithm. There are many other literatures focused on this field of study, which have done a lot of concrete works.…”

Creating and Debugging Performance CUDA C

“…searching near the best solution found so far) versus exploring (searching more widely) comes up repeatedly (in different guises) in search and optimisation. Zhu and Curry [37] again used CUDA this time with a GeForce GTX 280 and show it considerably sped up their ACO on a wide range of continuous optimisation benchmarks.…”