An astrophysics-inspired Grey wolf algorithm for numerical optimization and its application to engineering design problems. (October 2017)

• Record Type: Journal Article
• Authors: Kumar, Vijay; Kumar, Dinesh
• Is Part Of: Advances in engineering software. Volume 112(2017)
• Page Start: 231
• Page End: 254
• Publication Date: 2017-10
• Subjects: Meta-heuristics -- Grey wolf optimizer -- Astrophysics concept -- Function optimization -- Constrained design problems; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.05.008
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Three novel strategies are proposed which utilize the concepts of astrophysics and prey weight. The proposed strategies have been tested on thirteen standard benchmark test functions and compared with existing techniques. The proposed strategies provide better results than the other techniques for noisy and scalable environment. The effect of control parameter has also been investigated on the proposed strategies. The proposed strategies have been evaluated on seven well-known constrained engineering design problems. Abstract: In this paper, modified schemes are proposed for preventing a grey wolf optimizer (GWO) from premature exploration and convergence on optimization problems. Three novel strategies are developed to improve the performance of existing GWO. The first strategy uses the concept of prey weight. The second strategy uses the astrophysics concepts, which guide the grey wolves toward more promising areas of the search space. The beauty of this strategy is to let each grey wolf learn from not only movement of sun (symbolizes prey) in the search space but also the wolves are made to explore and exploit simultaneously. Third strategy combines the both, first and second strategies to take advantages of prey weight and astrophysics strategies. The proposed improvements in GWO have been evaluated on thirteen benchmark test functions. The performance of the proposed modifications has been compared with other five recently developed state-of-the-art techniques. The effects of scalability, noise, and control parameter have also been investigated. The statistical tests have been performed to validate the significance of modified variants. The proposed variants are also applied for seven well-known constrained engineering design problems. The experimental results depict the supremacy of the proposed modified algorithm in solving engineering design problems when compared with several existing techniques.