Abstract
An adaptive evolutionary strategy in standard particle swarm optimization is introduced. Adaptive evolution particle swarm optimization is constructed to improve the capacity of global search. A method based on adaptive evolution particle swarm optimization for identification of continuous system with time delay is proposed. The basic idea is that the identification of continuous system with time delay is converted to an optimization of continuous nonlinear function. The adaptive evolution particle swarm optimization is utilized to find an optimal solution of continuous nonlinear function. Convergence conditions are given by the convergence analysis based on discrete time linear dynamic system theory. Numerical simulation results show that the proposed method is effective for a general continuous system with time delay and the system of heat-transfer process of frequency induction furnace for melting copper.
Highlights
Frequency induction furnace has been developed into a kind of smelting equipment widely used
The continuous system identification methods based on particle swarm optimization (PSO) proposed in this paper are effective for general continuous system with time delay
The time delay of such a general continuous system needs approximate processing according to the identification method of second-order continuous system with time delay in literature [3]
Summary
Frequency induction furnace has been developed into a kind of smelting equipment widely used. The continuous system identification methods based on particle swarm optimization (PSO) proposed in this paper are effective for general continuous system with time delay. The position of the optimal solution of ith particle can be expressed as a D dimensional vector as follows: Pi = [pi pi2 pi3 ⋅ ⋅ ⋅ piD]. In order to avoid premature convergence phenomenon, many improved PSO algorithms have been proposed These algorithms tend to focus on improving inertia weight ω or introduce special mutation operation to particles [10]. If a linear decreasing inertia weight ω(t) is used in iterative process, the PSO algorithm has good global search capability in the beginning and has a good local search ability in the later stage [11]. (1) Initialization: generate N particles in D dimensional solution space which have random position and velocity vectors. Go to Step 3 and continue searching for the optimal solution until the criterion is satisfied
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