A hybrid DNA based genetic algorithm for parameter estimation of dynamic systems

Inspired by the evolutionary strategy and the biological DNA mechanism, a hybrid DNA based genetic algorithm (HDNA-GA) with the population update operation and the adaptive parameter scope operation is proposed for solving parameter estimation problems of dynamic systems. The HDNA-GA adopts the nucleotides based coding and some molecular operations. In HDNA-GA, three new crossover operators, replacement operator, transposition operator and reconstruction operator, are designed to improve the population diversity, and the mutation operator with adaptive mutation probability is applied to guarantee against stalling at local peak. Besides, the simulated annealing based selection operator is used to guide the evolution direction. In order to overcome the premature convergence drawbacks of GAs and enhance the algorithm global and local search abilities, the population update operator and the adaptive parameter scope operator are suggested. Numerous comparative experiments on benchmark functions and real-world parameter estimation problems in dynamic systems are conducted and the results demonstrate the effectiveness and efficiency of the HDNA-GA.

► Inspired by evolutionary strategy and DNA computing, a hybrid DNA based genetic algorithm is proposed.
► The proposed algorithm adopts the new crossover and mutation operators and the simulated annealing based selection operator.
► The optimization ability was greatly enhanced by employing the population update operator and the adaptive parameter scope operator.
► Comparative simulated experiments on the benchmark functions are performed.
► The real-world examples of dynamic system parameter estimation problems are discussed.