Particle Swarm Optimization And Its Applied Research In Nonlinear Regression Models

Particle Swarm Optimization (PSO) is characterized by real number coding, fewer parameters, fast convergence and highly parallell. This algorithm has become a new hotspot in computation intelligence field and has been widely used in comples domains, such as function optimizaiton problems, process control. However, PSO algoritnm has the problems which exist in other evolutionary algorithms, for instance, relapsing into local optimization easily and low accuracy computationin.To overcome the limitation of falling into local optimum easily and low accuracy computationin in PSO algorithm, this paper makes full use of the fluctuations ensue of extreme optimization and proposes a novel hybrid algorithm (EPSO algorithm), through introducing extremal optimization into PSO algorithm. For unconstrained problems, numerical simulation experiments on the fifteen benchmark functions are made by Matlab7.0. Simulation results show that the hybrid method is an effective way to locate global optima, and the hybrid algorithm shows more prominent for solving high-dimensional peak continuous optimization problems than unimodal benchmark functions.Secondly, the conceptual framework of constrained optimization algorithm equals to constraint processing technology and evolutionary algorithm is followed, we adopt augmented Lagrange multiplier method as a constraint handling technique, and the proposed hybrid algorithm is extended to handle constrained optimization problems. We testify the performance of the proposed algorithm on thirteen benchmark functions based on Matlab7.0. The results show that the novel method has achieved satisfactory results by smaller polulation size and less iteration.Finally, EPSO algorithm is used to solve the parameter estimation problem of nonlinear regression model through taking advantage of its essence is minimum squares critertion. Experimental results on two typical examples are truned out to be EPSO algorithm is convenient and easy to implement. EPSO algorithm is an efficient way to solve parameter estimation problem of nonlinear regression model.