多峰、高维的大规模优化问题是当前优化领域的研究热点． 文中以协同进化算法为框架，提出了一种融合多种搜索策略的差分进化大规模优化算法． 基于分解的思想，该算法首先利用自适应差分进化算子对子问题进行局部优化求解; 然后引入基于模拟退火的随机搜索机制提高算法的全局搜索能力，并结合局部搜索链对解空间进行深度搜索． 采用大规模优化标准函数对算法进行测试，结果表明，文中所提出的算法相比现有算法在平均值和最优解上均取得了更好的优化结果．

Large-scale optimization problem with multiple peaks and high dimension is a hot topic in current optimization research field.By using the co-evolutionary algorithm as the framework,this paper proposes a hybrid differential evolution ( DE) algorithm with multiple search strategies to solve the large-scale optimization problem.In this algorithm,firstly,based on the thought of decomposition,a self-adaptive DE operator is applied to a local optimization of sub-problems.Then,a random search mechanism based on simulated annealing is introduced to improve the algorithm s global search ability,and a local search chain is combined to search the solution space deeply.Finally,a set of benchmark functions is employed to evaluate the proposed algorithm.The results show that the algorithm is prior to the existing ones because it helps obtain better average value and optimized solution.