论文标题
分支的杂交和与非凸多物理优化的元启发式结合
The Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization
论文作者
论文摘要
将分支和绑定方法与多目标进化算法相结合的混合框架是针对非convex多目标优化的。杂交利用了两种优化策略的互补特征。多物原理进化算法旨在在分支和绑定过程中诱导紧密的下限和上限。诸如以这种方式得出的紧密界限可以减少必须解决的子问题的数量。分支和结合方法保证了框架的全局收敛性，并提高了多目标进化算法的搜索能力。提出了将NSGA-II和MOEA/D-DE作为多主体进化算法的混合框架的实现。数值实验验证混合算法的分支协同作用和绑定方法和多主体进化算法受益。
A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the two optimization strategies. A multiobjective evolutionary algorithm is intended for inducing tight lower and upper bounds during the branch and bound procedure. Tight bounds such as the ones derived in this way can reduce the number of subproblems that have to be solved. The branch and bound method guarantees the global convergence of the framework and improves the search capability of the multiobjective evolutionary algorithm. An implementation of the hybrid framework considering NSGA-II and MOEA/D-DE as multiobjective evolutionary algorithms is presented. Numerical experiments verify the hybrid algorithms benefit from synergy of the branch and bound method and multiobjective evolutionary algorithms.
