The Study of Improved Genetic Algorithm to Solve Flight Optimization Problem
Xiaofeng Wang, Si Shoukui, Xijing Sun
Abstract
Xiaofeng Wang, Si Shoukui, Xijing Sun
Abstract
To overcome the shortcomings of classical genetic algorithm for solving flight optimization problem, improved genetic algorithm is proposed. Structure of genetic algorithm is improved first. The mutation operation is separated from the crossover operation. The second, realization of genetic operation is modified. In crossover operation, the principal of best to best is used in the individual match for the crossover operation. The selection of the crossover point is made out by chaotic series. Crossover of one point is carried out to ensure precision of the algorithm, to weaken and avoid the oscillation in the process of optimization. In the mutation, a few genes are varied by chaotic operator to avoid prematurity of the algorithm. In the end, the improved genetic algorithm is compared with two usual genetic algorithms in solving flight optimization problem.
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To overcome the shortcomings of classical genetic algorithm for solving flight optimization problem, improved genetic algorithm is proposed. Structure of genetic algorithm is improved first. The mutation operation is separated from the crossover operation. The second, realization of genetic operation is modified. In crossover operation, the principal of best to best is used in the individual match for the crossover operation. The selection of the crossover point is made out by chaotic series. Crossover of one point is carried out to ensure precision of the algorithm, to weaken and avoid the oscillation in the process of optimization. In the mutation, a few genes are varied by chaotic operator to avoid prematurity of the algorithm. In the end, the improved genetic algorithm is compared with two usual genetic algorithms in solving flight optimization problem.
Key concepts: Crossover, Genetic algorithm, Meta-optimization, Computer science, Algorithm, Chaotic, Mathematical optimization, Mutation