A Parallel Framework for Multi-Population Cultural Algorithm and Its Applications in TSP

Unold, Olgierd; Tarnawski, Radosław

doi:10.1007/978-3-319-72926-8_39

Cited by 4 publications

(1 citation statement)

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“…The first stage of the algorithm consists of the MMAS being executed on the GPU and generating TSP routes which are then combined into the VRP solutions and improved using LS procedures during the second stage being executed on the CPU. The GPU-based MMAS is also one of components in the recently proposed parallel framework for the Multi-population Cultural Algorithm by Unold and Tarnawski [48].…”

Section: Recent Advancementsmentioning

confidence: 99%

Implementing a GPU-based parallel MAX–MIN Ant System

Skinderowicz

2020

Future Generation Computer Systems

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The MAX-MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore's law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another keycomponent -the tabu list structure -which is used in the ACO's solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3,795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1,002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

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Section: Recent Advancementsmentioning

confidence: 99%