원문정보
초록
영어
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.
목차
1. Introduction
2. Basic conceptions
3. Biogeography-based optimization and differential evolution
3.1. Biogeography-based optimization
3.2. Differential evolution
4. Hybrid algorithm BBO/DE
4.1. Objective function values modified
4.2. Hybrid Migration Operator
4.3. Hybrid algorithm BBO/DE
5. Experiment result
6. Conclusion
ACKNOWLEDGEMENTS
References