关键词: 大规模优化, 协同进化算法, 模拟退火, 差分进化, 局部搜索

Abstract:

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.

Key words: large-scale optimization, co-evolutionary algorithm, simulated annealing, differential evolution, local search