Document Type
Conference Proceeding
Publication Date
6-2011
Publication Title
24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems
Abstract
We present hardware testing of an evolutionary algorithm known as biogeography-based optimization (BBO) and extend it to distributed learning. BBO is an evolutionary algorithm based on the theory of biogeography, which describes how nature geographically distributes organisms. We introduce a new BBO algorithm that does not use a centralized computer, and which we call distributed BBO. BBO and distributed BBO have been developed by mimicking nature to obtain an algorithm that optimizes solutions for different situations and problems. We use fourteen common benchmark functions to obtain results from BBO and distributed BBO, and we also use both algorithms to optimize robot control algorithms. We present not only simulation results, but also experimental results using BBO to optimize the control algorithms of mobile robots. The results show that centralized BBO generally gives better optimization results and would generally be a better choice than any of the newly proposed forms of distributed BBO. However, distributed BBO allows the user to find a less optimal solution to a problem while avoiding the need for centralized, coordinated control.
Repository Citation
Scheidegger, Carre; Shah, Arpit; and Simon, Daniel J., "Distributed Learning with Biogeography-Based Optimization" (2011). Electrical and Computer Engineering Faculty Publications. 186.
https://engagedscholarship.csuohio.edu/enece_facpub/186
Original Citation
C. Scheidegger, A. Shah, and D. Simon. (2011). Distributed Learning with Biogeography-Based Optimization. 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, 203-215.
DOI
10.1007/978-3-642-21827-9_21
Version
Postprint
Publisher's Statement
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-21827-9_21
