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Improved Biogeography-Based Optimization Based on Affinity Propagation

School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China
Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan 250014, China
School of Mathematic and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250010, China
School of computer and Information Engineering, Heze University, Heze 274015, China
Shandong Police College, Jinan 250014, China
Author to whom correspondence should be addressed.
Academic Editors: Duccio Rocchini and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(8), 129;
Received: 26 May 2016 / Revised: 28 June 2016 / Accepted: 11 July 2016 / Published: 23 July 2016
(This article belongs to the Special Issue Spatial Ecology)
PDF [427 KB, uploaded 23 July 2016]


To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms. View Full-Text
Keywords: biogeography-based optimization; affinity propagation; memetic biogeography-based optimization; affinity propagation; memetic

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Wang, Z.; Liu, P.; Ren, M.; Yang, Y.; Tian, X. Improved Biogeography-Based Optimization Based on Affinity Propagation. ISPRS Int. J. Geo-Inf. 2016, 5, 129.

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