Abstract:

A new multiobjective optimization based on hybrid biogeographybased optimization (MOBBO) algorithm is proposed to solve multi-objective optimization problems. According to the evolutionary mechanism of BBO, the model of multi-objective evolutionary algorithm (MOEAs) which applies to BBO is built. In the model, the habitat suitability index, which combines with the Pareto dominance relation between the habitat individuals, is redefined. Moreover, a new mechanism based on the matrix of dynamic distance is set to maintain the distribution of population individuals. Simultaneously, according to the feature on multi-objective optimization, the self-adaptive method of determining the immigration rate and the emigration rate, dynamic migration strategy and the mutation strategy of piecewise logistic chaos are improved to achieve better convergence performance. Numerical experiments on ZDT and DTLZ test functions show that MOBBO is competitive with current other MOEAs on the convergence and the distribution, and is capable of solving the complex high dimensional multi-objective opti-mization problems (MOPs) more effectively and efficiently.