A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation. Issue 2 (28th January 2021)

• Record Type: Journal Article
• Authors: Li, Qijun; Ning, Huifeng; Gong, Jun; Li, Xiao; Dai, Baolin
• Is Part Of: Applied artificial intelligence. Volume 35:Issue 2(2021)
• Page Start: 171
• Page End: 191
• Publication Date: 2021-01-28
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2020.1848276
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: 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.