Optimization of PID parameters based on genetic algorithm and interval algorithm
Shao Xiao-gen, Li-qing Xiao, Chengchun Han
Abstract
Shao Xiao-gen, Li-qing Xiao, Chengchun Han
Abstract
To overcome simple genetic algorithm's defects of worse local searching ability and premature convergence, a hybrid algorithm of genetic algorithm and interval algorithm was proposed, and applied to parameters optimization of PID controller by employing interval algorithm in population initialization of genetic algorithm. The simulation and experimental results show that the new algorithm is better than simple genetic algorithm, which can improve the convergence speed, overcome the premature convergence phenomena, reduce the influence of random initial population, improve the convergence precision, and has excellent convergence performance and optimization ability.
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To overcome simple genetic algorithm's defects of worse local searching ability and premature convergence, a hybrid algorithm of genetic algorithm and interval algorithm was proposed, and applied to parameters optimization of PID controller by employing interval algorithm in population initialization of genetic algorithm. The simulation and experimental results show that the new algorithm is better than simple genetic algorithm, which can improve the convergence speed, overcome the premature convergence phenomena, reduce the influence of random initial population, improve the convergence precision, and has excellent convergence performance and optimization ability.
Key concepts: Initialization, Premature convergence, Population-based incremental learning, Meta-optimization, Genetic algorithm, Convergence (economics), Interval (graph theory), Algorithm