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Transactions of the Institute of Measurement and Control


Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational time


This material is based upon work supported by the National Science Foundation under grant number 0826124, the National Natural Science Foundation of China under grant numbers 61305078, 61074032 and 61179041 and the Shaoxing City Public Technology Applied Research Project under grant number 2013B70004.

Original Citation

H. Ma, M. Fei, D. Simon and Z. Chen, "Biogeography-based optimization in noisy environments," Transactions of the Institute of Measurement and Control, vol. 37, pp. 190-204, 2015.