Cooperative Coevolution with global search for large scale global optimization

摘要

To improve the performance of EAs on large scale numerical optimization problems, a number of techniques have been invented, among which, Cooperative Coevolution (CC in short) is obviously a promising one. But sometimes CC is easy to lead to premature convergence in large scale global optimization. In this paper, a Cooperative Coevolution Evolutionary Algorithm (CCEA in short) with global search (CCGS) is presented to handle large scale global optimization (LSGO) problems. The performance of CCGS is evaluated on the test functions provided for the CEC 2012 competition and special session on Large Scale Global Optimization. The experiment results show that this technique is more effective than CCEAs without global search.