期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2014
卷号：7
期号：3
页码：265-274
DOI：10.14257/ijhit.2014.7.3.25
出版社：SERSC
摘要：A new hybrid Optimization Algorithm is proposed to solve Constrained Multi-objective Optimization Problems (CMOPs). The algorithm is named BBO/DE which combines the exploitation ability of Biogeography-based Optimization (BBO) and the exploration ability of Differential Evolution (DE). Meanwhile distance measures and adaptive penalty functions are adopted to handle the constraints so that optimal solutions in the infeasible space can be searched effectively. In addition, the feasible archive is applied to store the non-dominated feasible solutions obtained so far and is updated based on crowding-distance. Experiment results demonstrate that the proposed hybrid algorithm BBO/DE can approximate the true Pareto front and has better distribution.
