Welcome to the InfoSci Platform
Reference Hub10
A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions

A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions

Ibrahim Aljarah, Simone A. Ludwig
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 23
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781466691568|DOI: 10.4018/IJSIR.2016010102
Cite Article Cite Article

MLA

Aljarah, Ibrahim, and Simone A. Ludwig. "A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions." IJSIR vol.7, no.1 2016: pp.32-54. http://doi.org/10.4018/IJSIR.2016010102

APA

Aljarah, I. & Ludwig, S. A. (2016). A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions. International Journal of Swarm Intelligence Research (IJSIR), 7(1), 32-54. http://doi.org/10.4018/IJSIR.2016010102

Chicago

Aljarah, Ibrahim, and Simone A. Ludwig. "A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions," International Journal of Swarm Intelligence Research (IJSIR) 7, no.1: 32-54. http://doi.org/10.4018/IJSIR.2016010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Glowworm Swarm Optimization (GSO) is one of the common swarm intelligence algorithms, where GSO has the ability to optimize multimodal functions efficiently. In this paper, a parallel MapReduce-based GSO algorithm is proposed to speedup the GSO optimization process. The authors argue that GSO can be formulated based on the MapReduce parallel programming model quite naturally. In addition, they use higher dimensional multimodal benchmark functions for evaluating the proposed algorithm. The experimental results show that the proposed algorithm is appropriate for optimizing difficult multimodal functions with higher dimensions and achieving high peak capture rates. Furthermore, a scalability analysis shows that the proposed algorithm scales very well with increasing swarm sizes. In addition, an overhead of the Hadoop infrastructure is investigated to find if there is any relationship between the overhead, the swarm size, and number of nodes used.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.