GPU-based Parallel Implementation of Swarm Intelligence Algorithms - 1st Edition - ISBN: 9780128093627, 9780128093641

GPU-based Parallel Implementation of Swarm Intelligence Algorithms

1st Edition

Authors: Ying Tan
eBook ISBN: 9780128093641
Paperback ISBN: 9780128093627
Imprint: Morgan Kaufmann
Published Date: 31st March 2016
Page Count: 256
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GPU-based Parallel Implementation of Swarm Intelligence Algorithms combines and covers two emerging areas attracting increased attention and applications: graphics processing units (GPUs) for general-purpose computing (GPGPU) and swarm intelligence. This book not only presents GPGPU in adequate detail, but also includes guidance on the appropriate implementation of swarm intelligence algorithms on the GPU platform.

GPU-based implementations of several typical swarm intelligence algorithms such as PSO, FWA, GA, DE, and ACO are presented and having described the implementation details including parallel models, implementation considerations as well as performance metrics are discussed. Finally, several typical applications of GPU-based swarm intelligence algorithms are presented. This valuable reference book provides a unique perspective not possible by studying either GPGPU or swarm intelligence alone.

This book gives a complete and whole picture for interested readers and new comers who will find many implementation algorithms in the book suitable for immediate use in their projects. Additionally, some algorithms can also be used as a starting point for further research.

Key Features

  • Presents a concise but sufficient introduction to general-purpose GPU computing which can help the layman become familiar with this emerging computing technique
  • Describes implementation details, such as parallel models and performance metrics, so readers can easily utilize the techniques to accelerate their algorithmic programs
  • Appeals to readers from the domain of high performance computing (HPC) who will find the relatively young research domain of swarm intelligence very interesting
  • Includes many real-world applications, which can be of great help in deciding whether or not swarm intelligence algorithms or GPGPU is appropriate for the task at hand


Researchers, engineers, graduates and senior undergraduates with interests in parallel implementation of novel swarm intelligence algorithms like particle swarm optimization, fireworks algorithms, genetic algorithms, and their applications.

Table of Contents

  • Dedication
  • Preface
  • Acknowledgments
  • Acronyms
  • Chapter 1: Introduction

    • 1.1 Swarm Intelligence Algorithms (SIAs)
    • 1.2 Graphics Processing Units (GPUs)
    • 1.3 SIAs and GPUs
    • 1.4 Some Perspectives
    • 1.5 Organization
  • Chapter 2: GPGPU: General-Purpose Computing on the GPU

    • 2.1 Introduction
    • 2.2 GPGPU Development Platforms
    • 2.3 Compute Unified Device Architecture (CUDA)
    • 2.4 Open Computing Language (OpenCL)
    • 2.5 Programming Techniques
    • 2.6 Some Discussions
    • 2.7 Summary
  • Chapter 3: Parallel Models

    • 3.1 Previous Work
    • 3.2 Basic Guide for Parallel Programming
    • 3.3 GPU-Oriented Parallel Models
    • 3.4 Naïve Parallel Model
    • 3.5 Multi-Kernel Parallel Model
    • 3.6 All-GPU Parallel Model
    • 3.7 Island Parallel Model
    • 3.8 Summary
  • Chapter 4: Performance Metrics

    • 4.1 Parallel Performance Metrics
    • 4.2 Algorithm Performance Metrics
    • 4.3 Rectified Efficiency
    • 4.4 Case Study
    • 4.5 Summary
  • Chapter 5: Implementation Considerations

    • 5.1 Float-Point
    • 5.2 Memory Accesses
    • 5.3 Random Number Generation
    • 5.4 Branch Divergence
    • 5.5 Occupancy
    • 5.6 Summary
  • Chapter 6: GPU-Based Particle Swarm Optimization

    • 6.1 Introduction
    • 6.2 Particle Swarm Optimization
    • 6.3 GPU-Based PSO for Single-Objective Optimization
    • 6.4 GPU-Based PSO for Multiple-Objective Optimization
    • 6.5 Remarks
    • 6.6 Summary
  • Chapter 7: GPU-Based Fireworks Algorithm

    • 7.1 Introduction
    • 7.2 Fireworks Algorithms (FWA)
    • 7.3 GPU-Based Fireworks Algorithm
    • 7.4 Summary
  • Chapter 8: Attract-Repulse Fireworks Algorithm Using Dynamic Parallelism

    • 8.1 Introdu


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© Morgan Kaufmann 2016
Morgan Kaufmann
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About the Author

Ying Tan

Ying Tan is a professor and PhD advisor at the School of Electronics Engineering and Computer Science of Peking University, and director of Computational Intelligence Laboratory at Peking University (PKU). He received his BEng from the EEI, MSc from Xidian Univ., and PhD from Southeast Univ., in 1985, 1988, and 1997, respectively. From 1997, he was a postdoctoral fellow then an associate professor at University of Science and Technology of China (USTC), then served as director of Institute of Intelligent Information Science and a full professor since 2000. He worked with the Chinese University of Hong Kong (CUHK) in 1999 and 2004-2005. He was elected for the 100 talent program of the Chinese Academy of Science (CAS) in 2005

Affiliations and Expertise

Professor, National Laboratory on Machine Perception, Department of Intelligence Science, School of Electronic Engineering and Computer Science, Peking University, Beijing, China

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