Hybrid biogeography-based evolutionary algorithms

Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The new hybrid EAs include two approaches to hybridization: (1) iteration-level hybridization, in which various EAs and BBO are executed in sequence; and (2) algorithm-level hybridization, which runs various EAs independently and then exchanges information between them using ideas from biogeography. Our empirical study shows that the new hybrid EAs significantly outperforms their constituent algorithms with the selected tuning parameters and generation limits, and algorithm-level hybridization is generally better than iteration-level hybridization. Results also show that the best new hybrid algorithm in this paper is competitive with the algorithms from the 2013 CEC competition. In addition, we show that the new hybrid EAs are generally robust to tuning parameters. In summary, the contribution of this paper is the introduction of biogeography-based hybridization strategies to the EA community

Differential Evolution Biogeography Based Optimization for Linear Phase Fir Low Pass Filter Design

This paper presents an efficient way of designing Linear Phase Finite Impulse Response (FIR) Filter using hybrid Differential Evolution (DE) and Biogeography based optimization (BBO) algorithms. DE is a fast and robust evolutionary algorithm tool for global optimization. On the other hand, BBO uses migration operator to share information among solutions. FIR filter of order 20 is designed using fitness function that is based on minimization of maximum ripples in pass band and stop band of the filter response. The result obtained from Differential Evolution Biogeography Based Optimization (DEBBO) for the FIR low pass filter is good in convergence speed and solution quality in terms of pass band ripple, stop band ripple, transition width. Keywords: DE, BBO, DEBBO, Convergence, FIR Filter