A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation

Sine-cosine algorithm (SCA) has found a widespread application in various engineering optimization problems. However, SCA suffers from premature convergence and insufficient exploitation. Cylindricity error evaluation is a typical engineering optimization problem related to the quality of cylindrical parts. A hybrid greedy sine-cosine algorithm with differential evolution (HGSCADE) is developed in this paper to solve optimization problems and evaluate cylindricity error. HGSCADE integrates the SCA with the opposition-based population initialization, the greedy search, the differential evolution (DE), the success history-based parameter adaptation, and the Levy flight-based local search. HGSCADE is tested on the CEC2014 benchmark functions and is employed in cylindricity error evaluation. The results show the superiority of HGSCADE to other state-of-the-art algorithms for the benchmark functions and cylindricity error evaluation.