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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Hybrid CODBA-II Algorithm Coupling a Co-evolutionary Decomposition-based Algorithm with Local Search Method to solve Bi-level Combinatorial Optimization
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Hybrid CODBA-II Algorithm Coupling a Co-evolutionary Decomposition-based Algorithm with Local Search Method to solve Bi-level Combinatorial Optimization

机译：混合Codba-II算法耦合具有本地搜索方法的共进进化分解的算法解决双级组合优化

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Bi-level optimization problems (BLOPs) are a class of challenging problems with two levels of optimization tasks. The usefulness of bi-level optimization in designing hierarchical decision processes prompted several researchers, in particular the evolutionary computation community, to pay more attention to such kind of problems. Several solution approaches have been proposed to solve these problems; however, most of them are restricted to the continuous case. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition based-Algorithm (CODBA-II) to solve combinatorial bi-level problems. CODBA-II scheme has been able to improve the bi-level performance and to bring down the computational expense significantly as compared to other competitive approaches within this research area. In this paper, we present an extension of the recently proposed CODBA-II algorithm. The improved version, called CODBA-IILS, further improves the algorithm by incorporating a local search process to both upper and lower levels in order to help in faster convergence of the algorithm. The improved results have been demonstrated on two different sets of test problems based on the bi-level production-distribution problems in supply chain management, and comparison results against the contemporary approaches are also provided.
机译：双级优化问题（跳闸）是一类具有两级优化任务的具有挑战性问题。双层优化在设计分层决策过程中的有用性提示了几个研究人员，特别是进化计算界，更加关注这种问题。提出了一些解决方案方法来解决这些问题;然而，大多数被限制在连续的情况下。通过这种观察，我们最近提出了一种基于共进分解的分解算法（CodBA-II）来解决组合双级问题。与本研究领域的其他竞争方法相比，CODBA-II计划已经能够提高双层性能，并显着降低计算费用。在本文中，我们展示了最近提出的CODBA-II算法的扩展。通过将本地搜索过程结合到高级和下限，进一步改进了CODBA-IIL的改进版本，以帮助算法更快地收敛。基于供应链管理中的双级生产分布问题，还对两组不同的测试问题进行了证明，还提供了对当代方法的比较结果。

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