Fast parallel memetic algorithm for vector quantization based for reconfigurable hardware and softcore processor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel parallel memetic algorithm (MA) architecture for the design of vector quantizers is presented in this paper. The architecture contains a number of modules operating memetic optimization concurrently. Each module uses steady-state genetic algorithm (GA) for global search, and K-means algorithm for local refinement. A shift register based circuit for accelerating mutation and crossover operations for steady state GA operations is adopted in the design. A pipeline architecture for the hardware implementation of K-means algorithm is also used. The proposed architecture is embedded in a softcore CPU, and implemented on a field programmable logic array (FPGA) device for physical performance measurement.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings
Pages479-488
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2010 Jul 21
Event1st International Conference on Advances in Swarm Intelligence, ICSI 2010 - Beijing, China
Duration: 2010 Jun 122010 Jun 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6145 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Advances in Swarm Intelligence, ICSI 2010
CountryChina
CityBeijing
Period10/6/1210/6/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yu, T. Y., Hwang, W. J., & Chiang, T. C. (2010). Fast parallel memetic algorithm for vector quantization based for reconfigurable hardware and softcore processor. In Advances in Swarm Intelligence - First International Conference, ICSI 2010, Proceedings (PART 1 ed., pp. 479-488). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6145 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-13495-1_59