Elite Multi-Group Differential Evolution

An Elite Multi-Group Differential Evolution algorithm for unconstrained single objective optimization is proposed. In the novel algorithm, the population is divided into sub-groups with different parameters setting to balance the global and local search ability. The good information collected in the search process is exchanged among groups. Experiments are conducted on seven commonly used benchmark functions and two new constructed harder test functions which are useful to test the local search ability of the algorithms and the proposed algorithm shows its effectiveness and efficiency.

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