2009Unpublished venueRequires access

Optimization of PID parameters based on genetic algorithm and interval algorithm

Shao Xiao-gen, Li-qing Xiao, Chengchun Han

Open publisher page 11 citations

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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What this paper is about

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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OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available 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.

Key concepts: Initialization, Premature convergence, Population-based incremental learning, Meta-optimization, Genetic algorithm, Convergence (economics), Interval (graph theory), Algorithm

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