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

Ibrahim Aljarah and Simone A. Ludwig
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Ibrahim Aljarah: Department of Business Information Technology, The University of Jordan, Amman, Jordan
Simone A. Ludwig: Department of Computer Science, North Dakota State University, Fargo, ND, USA

International Journal of Swarm Intelligence Research (IJSIR), 2016, vol. 7, issue 1, 32-54

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.

Date: 2016
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