2014Applied Mechanics and MaterialsRequires access

The Optimization Design of Multistage Hybrid Planetary Transmission Based on Improved Genetic Algorithm

Ji Ming Tian, Xin Tan

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Abstract

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.

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What this paper is about

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.

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Available abstract

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.

Key concepts: Genetic algorithm, Mathematical optimization, Meta-optimization, Algorithm, Constraint (computer-aided design), Stability (learning theory), Population-based incremental learning, Computer science

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