Parameter Estimation of Chaotic System Based on Hybrid Swarm Intelligence Optimization Algorithm

doi:10.19596/j.cnki.1001-246x.7909

Abstract

Abstract: Estimation of unknown parameters of chaotic systems is a primary problem in chaos control and synchronization, which could be transformed into an optimization problem with multi-dimensional parameter space by constructing a proper fitness function. A hybrid swarm optimization algorithm combining improved bare bones particle swarm optimization algorithm and improved differential evolution is proposed. Particle position update mechanism, mutation operation, crossover operation, and crossover probability factor design are improved, taking into account both diversity of population and convergence rate of algorithm. On this basis, fusion optimization strategy of bare bones particles swarm optimization algorithm and differential evolution algorithm is discussed. Co-evolution of two algorithms is realized. To test algorithm optimization performance, simulation experiments were carried out with six benchmark functions. It shows that the proposed algorithm has powerful global optimizing ability, more stability, fast convergence speed and higher optimizing precision and so on. Lorenz chaotic system was taken as an example to estimate three unknown system parameters.

Key words: chaotic system, parameter estimation, bare bones particle swarm optimization, differential evolution

CLC Number:

TP301.6

SHI Jianping, LI Peisheng, LIU Guoping. Parameter Estimation of Chaotic System Based on Hybrid Swarm Intelligence Optimization Algorithm[J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 36(5): 621-630.

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