Particle Swarm Optimization For Design Research Articles

System Identification of Heat-Transfer Process of Frequency Induction Furnace for Melting Copper Based on Particle Swarm Algorithm

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.

Simulation and experimental analysis for hysteresis behavior of a piezoelectric actuated micro stage using modified charge system search

The main goal of this study is to investigate the hysteresis behavior of a piezoelectric actuated micro stage. The hysteresis of piezoelectric actuators (PA) is formulated using the generalized Duhem model (GDM) and a modified charge system search (CSS) is proposed to identify the hysteresis model. Different from the present CSS method, the proposed method applies a construction factor to prevent converging to a local optimum. The modified CSS is used to evaluate the parameters of the GDM to compare with the particle swarm optimization (PSO). The use of MCSS-based optimization is superior to the CSS and the PSO for identification of the GDM. The experimental result also validated the modeling correctness using the proposed method.

Particle Swarm Optimization for Identification of PEMFC Generation System Fuzzy Model

This paper proposes particle swarm optimization (PSO) for identification of the Proton Exchange Membrane Fuel Cells (PEMFC) generation systems fuzzy model. The PEM fuel cell generation system efficiency decreases as its output power increases. Thus, an optimum efficiency should exist and should result in a cost-effective PEM fuel cell generation system. The PEMFC generation system cost and efficiency fuzzy model were build, we use the PSO as an optimization engine to indentify the fuzzy model. The simulation results were presented and the results show that we may minimize the total cost of the generation system by using the PSO.