Improved Biogeography-Based Optimization Based on Affinity Propagation
AbstractTo 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
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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.
Wang Z, Liu P, Ren M, Yang Y, Tian X. Improved Biogeography-Based Optimization Based on Affinity Propagation. ISPRS International Journal of Geo-Information. 2016; 5(8):129.Chicago/Turabian Style
Wang, Zhihao; Liu, Peiyu; Ren, Min; Yang, Yuzhen; Tian, Xiaoyan. 2016. "Improved Biogeography-Based Optimization Based on Affinity Propagation." ISPRS Int. J. Geo-Inf. 5, no. 8: 129.
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