In this paper, we propose a memetic multi-agent optimization (MeMAO) paradigm to enhance the search efficacy of classical EAs (i.e., Differential Evolution (DE)) in solving the complex optimization problems. The essential backbone of MeMAO is a recently proposed memetic multi-agent learning system wherein agents acquire increasing learning capabilities by interacting with the environment mainly in a reinforcement learning manner. Differing from MeMAS, the particular interest of MeMAO is placed on addressing the specific challenges when applying classical EAs to optimize the high dimensional optimization problems with a "low effective dimensionality". To achieve this, the target optimization problem is firstly re-formulated into multiple low dimensional tasks via random embedding methods. Further, MeMAO employs DE as the fundamental population based evolutionary solver for multiple agents to optimize multiple low dimensional tasks in a multi-agent scenario. Importantly, MeMAO constructs the social interaction mechanisms among multiple agents, hence improves their convergence speed for solving the target optimization problem by sharing the beneficial information across multiple agents. Lastly, to testify the efficacy of the proposed MeMAO, comprehensive empirical studies on 8 synthetic optimization problems with a dimensionality of 2,000 are provided.