Enhanced differential evolution with generalised opposition-based learning and orientation neighbourhood mining (Q2221584)

Summary: This paper presents an enhanced differential evolution (DE) algorithm which employs two strategies including generalised opposition-based learning (GOBL) and orientation neighbourhood mining (ONM). The two strategies are very helpful to balance global search ability and local search ability of algorithm. To verify the performance of our approaches, seven famous benchmark functions are utilised. Conducted experiments indicate that the enhanced algorithm performs significantly better than, or at least comparable to, several state-of-the-art DE variants.