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International Conference on Intelligent Software Methodologies, Tools, and Techniques

SoMeT 2014: Intelligent Software Methodologies, Tools and Techniques pp 25–38Cite as

Easy and Concise Programming for Low-Level Hybridization of PSO-GA

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 513))

Abstract

Responding to the difficulties of implementing Low-Level Hybridization (LLH) of meta-heuristics, this paper introduces a reusable software for the algorithm design and development. This paper proposes three implementation frameworks for the LLH of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Then, with attempt to support a more effective programming environment, a set of scripting language constructs based on the proposed implementation frameworks is developed. For evaluation, twelve algorithms that composed of nine LLHs and three single PSO have been coded and executed with the scripting language. The results demonstrate that the scripting language is anticipated for enabling of an easier and more concise programming for effective rapid prototyping and testing of the algorithms.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Ipoh, Perak, Malaysia

    Suraya Masrom

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia

    Siti Zaleha Zainal Abidin & Nasiroh Omar

Authors
  1. Suraya Masrom
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  2. Siti Zaleha Zainal Abidin
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  3. Nasiroh Omar
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Corresponding author

Correspondence to Suraya Masrom .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Johor Baharu, Malaysia

    Ali Selamat

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Masrom, S., Zainal Abidin, S.Z., Omar, N. (2015). Easy and Concise Programming for Low-Level Hybridization of PSO-GA. In: Fujita, H., Selamat, A. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2014. Communications in Computer and Information Science, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-17530-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-17530-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17529-4

  • Online ISBN: 978-3-319-17530-0

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