Abstract
This study proposes a new selection method called trisection population for genetic algorithm selection operations. In this new algorithm, the highest fitness of 2N/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.
Highlights
This study proposes a new selection method called trisection population for genetic algorithm selection operations
GA [1] are a type of optimization method that is based on biological evolution
Many researchers have proposed strategies to improve these algorithms. These studies have investigated the design of encoding genetic algorithms [5, 6], genetic operations improvement [7, 8], combination with other optimization algorithms [9,10,11], and so forth
Summary
GA (genetic algorithms) [1] are a type of optimization method that is based on biological evolution. The selection operator of GA was improved based on existing genetic algorithm. This study proposed that a genetic algorithm crossover and mutation probability adaptive methods are based on the characteristics of arc tangent function. An improved GA was used to solve the layout problem of drilling equipment for deep-water, semisubmersible platforms. Calculation results confirm that the improved algorithm is quite efficient for solving the NPhard layout problem. In standard GA, offspring is generated by parent individuals through the selection operator, crossover operator, and mutation operator. All parent individuals participate in the generation procedure of offspring This is not useful for excellent individuals’ evolution at rapid speed.
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