Paper Info
Title
A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions
Abstract
AbstractGlowworm 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.
Year
DOI
Venue
2016
10.4018/IJSIR.2016010102
Periodicals
Keywords
Field
DocType
Big Data, MapReduce, Multi-modal Functions, Optimization
Mathematical optimization,Swarm behaviour,Computer science,Swarm intelligence,Glowworm swarm optimization,Multi-swarm optimization,Parallel programming model,Artificial intelligence,Big data,Machine learning,Speedup,Scalability
Journal
Volume
Issue
ISSN
7
1
1947-9263
Citations 
PageRank 
References 
5
0.44
11
Authors
2
Name
Order
Citations
PageRank
Ibrahim Aljarah170333.62
Simone A Ludwig21309179.41