Memetic algorithm with adaptive local search depth for large scale global optimization

Memetic algorithms (MAs) have been recognized as an effective algorithm framework for solving optimization problems. However, the exiting work mainly focused on the improvement for search operators. Local Search Depth (LSD) is a crucial parameter in MAs, which controls the computing resources assigned for local search. In this paper, an Adaptive Local Search Depth (ALSD) strategy is proposed to arrange the computing resources for local search according to its performance dynamically. A Memetic Algorithm with ALSD (MA-ALSD) is presented, its performance and the effectiveness of ALSD are testified via experiments on the LSGO test suite issued in CEC´2012.