Applying a New Adaptive Genetic Algorithm to Study the Layout of Drilling Equipment in Semisubmersible Drilling Platforms. (27th May 2015)

• Record Type: Journal Article
• Authors: Xiao, Wensheng; Wu, Lei; Tian, Xue; Wang, Jingli
• Other Names: Gu Jason Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2015(2015)
• Publication Date: 2015-05-27
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2015/146902
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : This study proposes a new selection method called trisection population for genetic algorithm selection operations. In this new algorithm, the highest fitness of 2 N /3 parent individuals is genetically manipulated to reproduce offspring. This selection method ensures a high rate of effective population evolution and overcomes the tendency of population to fall into local optimal solutions. Rastrigin's test function was selected to verify the superiority of the method. Based on characteristics of arc tangent function, a genetic algorithm crossover and mutation probability adaptive methods were proposed. This allows individuals close to the average fitness to be operated with a greater probability of crossover and mutation, while individuals close to the maximum fitness are not easily destroyed. This study also analyzed the equipment layout constraints and objective functions of deep-water semisubmersible drilling platforms. The improved genetic algorithm was used to solve the layout plan. Optimization results demonstrate the effectiveness of the improved algorithm and the fit of layout plans.