Simplified swarm optimization with differential evolution mutation strategy for parameter search

Authors:
Po-Chun Chang;Wei-Chang Yeh
Affiliations:
University of Technology Sydney;University of Technology Sydney and National Tsing Hua University, Hsinchu, Taiwan
Venue:
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
Year:
2013

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Abstract

In practical applications, solving dynamic optimization problem is a challenging field. In recent decades, the optimization approach is not merely dealing with unimodal functions, but also multimodal functions. Even more, the performance of optimization algorithms is affected by the size of dimensional problems. Some algorithms have shown excellent search abilities with small dimensional problems, but they become inadequate with large dimensional space. The opposite may also be true. This paper proposed a robust global optimization algorithm, SSODE - SSO (Simplified Swarm Optimization) with DE (Differential Evolution) mutation strategy. SSO was initially proposed to overcome the shortcoming of PSO (Particle Swarm Optimization) for discrete data space. DE is the meta-heuristic based evolutionary algorithm which is used for optimizing multi-dimensional real-value functions. Here, we performed two experiments on SSODE algorithm and compared it with the original DE and SSO. One was performed on search of parameter values for SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel. The other experiment was performed on five common benchmark functions.