Cooperative Co-evolutionary algorithms are very popular to solve large-scale problems. A significant part of these algorithms is the decomposition of the problems according to the variables interaction. In this paper, an approach based on a memetic scheme, where its local stage (and not the global stage) is guided by the decomposition method (Local Cooperative Search LoCoS), is presented to solve large-scale constrained optimization problems. Two decomposition methods are tested: the improved version of the Variable Interdependence Identification for Constrained problems and Differential Grouping version 2. A recently-proposed benchmark with eighteen test problems with different features is solved to assess the performance of LoCoS when compared against a similar memetic algorithm but without decomposition and also against a state-of-the-art cooperative co-evolutionary algorithm. The results show a faster convergence, better final results and higher feasibility ratio by LoCosS with respect to the values provided by the compared algorithms.

协同协同进化算法在解决大规模问题方面非常流行。这些算法的重要部分是根据变量交互作用对问题进行分解。本文提出了一种基于模因方案的方法，其局部阶段（而不是全局阶段）由分解方法（局部合作搜索LoCoS）指导，以解决大规模约束优化问题。测试了两种分解方法：约束问题的变量相互依赖标识的改进版本和差分分组版本2。解决了最近提出的具有18个具有不同功能的测试问题的基准，以与类似的模因算法但不进行分解以及最先进的协作协同进化算法相比，评估LoCoS的性能。结果表明，相对于比较算法提供的值，LoCosS的收敛速度更快，最终结果更好，并且可行性比更高。