An astrophysics-inspired Grey wolf algorithm for numerical optimization and its application to 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.

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