Constrained Optimization Based on Epsilon Constrained Biogeography-Based Optimization

Abstract

A new epsilon constrained biogeography-based optimization is proposed to solve constrained optimization problems. In the proposed algorithm, the epsilon constrained method is utilized to handle the constraints. Simultaneously, based on the feature of epsilon constrained method, a new ordering rule based on epsilon constrained is used to obtain the immigration rate and emigration rate. Additionally, a new dynamic migration strategy is shown to enhance the search ability of migration mechanism. Eventually, with the purpose of improving the precision of convergence, the piecewise logistic chaotic map is introduced to improve the variation mechanism. Numerical experiments on 13 well-known benchmark test function have shown that the proposed algorithm is competitive with other optimization algorithms. Furthermore, the proposed algorithm can avoid effectively the convergence before the optimal results have been found, and balance the exploitation and the exploration.