A New Adaptive Hybrid Algorithm for Large-Scale Global Optimization

Abstract

Large-scale global optimization (LSGO) problems are one of most difficult optimization problems and many works have been done for this kind of problems. However, the existing algorithms are usually not efficient enough for difficult LSGO problems. In this paper, we propose a new adaptive hybrid algorithm (NAHA) for LSGO problems, which integrates the global search, local search and grouping search and greatly improves the search efficiency. At the same time, we design an automatic resource allocation strategy which can allocate resources to different optimization strategies automatically and adaptively according to their performance and different stages. Furthermore, we propose a self-learning parameter adjustment scheme for the parameters in local search and grouping search, which can automatically adjust parameters. Finally, the experiments are conducted on CEC 2013 LSGO competition benchmark test suite and the proposed algorithm is compared with several state-of-the-art algorithms. The experimental results indicate that the proposed algorithm is pretty effective and competitive.