Distributed Biogeography-Based Optimization


Dan Simon


Biogeography-based optimization (BBO) is an evolutionary optimization algorithm. Distributed BBO, referred to here as DBBO, is the extension of BBO to the situation when candidate solutions to an optimization problem cannot communicate with a central processor, and cannot communicate among themselves on a consistent basis. This situation arises in many practical applications, such as peer-to-peer networking, and robots’ whose communication is constrained by time or space.


The paper below proposes a DBBO algorithm, applies it to benchmark problems, and applies it to simulated and experimental robot controller optimization. The paper also derives a Markov model for DBBO. The MATLAB software that was used to derive the Markov model results can be downloaded in a zip file.




D. Simon, A. Shah, and C. Scheidegger, “Distributed learning with biogeography-based optimization: Markov modeling and robot control,” Swarm and Evolutionary Computation, vol. 10, pp. 12–24, June 2013 - pdf, 430 KB


A preliminary version of the paper was presented at a conference:


C. Scheidegger, A. Shah, and D. Simon, “Distributed Learning with Biogeography-Based Optimization,” 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, pp. 203-215, June 2011 - pdf, 382 KB


Professor Simon’s Home Page


Department of Electrical and Computer Engineering


Cleveland State University


Last Revised: December 13, 2013