Orthogonal learning particle swarm optimization for power electronic circuit optimization with free search range

Power electronic circuit (PEC) always consists of a number of components such as resistors, capacitors, and inductors which have to be optimized in order to obtain good circuit performance. In current studies, the search ranges of these components are always pre-defined carefully by expert designers, making it difficult for practical applications. In this paper, the search space is freely set to the commonly used ranges and an efficient orthogonal learning particle swarm optimization (OLPSO) is applied to optimally design the PEC with such search space. OLPSO uses an orthogonal learning (OL) strategy for PSO to discover useful information that lies in the personal historical best experience and the neighborhood's best experience via orthogonal experimental design. Therefore, OLPSO can construct a more promising and efficient exemplar to guide particle to fly better towards the global optimal region. OLPSO is implemented to optimize the design of a buck regulator in PEC. The optimized results are compared with those obtained by using a genetic algorithm (GA) approach and those obtained by using PSO with traditional learning strategy. Results show that the OLPSO algorithm is more promising in the design and optimization of the PEC with large search space. Moreover, the simulations results demonstrate the advantages of OLPSO by showing that the circuit optimized by OLPSO exhibits better startup and large-signal disturbance performance when compared with the one optimized by GA.

[1]  Xinling Shi,et al.  A Modified Particle Swarm Optimizer with Dynamical Inertia Weight , 2009, ICFIE.

[2]  Bimal K. Bose.  Energy, environment, and advances in power electronics , 2000 .

[3]  Gerald Jay Sussman,et al.  Heuristic techniques in computer-aided circuit analysis , 1975 .

[4]  Jun Zhang,et al.  Implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms , 2001 .

[5]  Rob A. Rutenbar,et al.  OASYS: a framework for analog circuit synthesis , 1989, Proceedings., Second Annual IEEE ASIC Seminar and Exhibit,.

[6]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[7]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[8]  Rob A. Rutenbar,et al.  Synthesis of high-performance analog circuits in ASTRX/OBLX , 1996, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[9]  Tore Undeland,et al.  Power Electronics: Converters, Applications and Design , 1989 .

[10]  Jun Zhang,et al.  Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms , 2007, IEEE Transactions on Evolutionary Computation.

[11]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2011, IEEE Trans. Evol. Comput..

[12]  Malik E. Elbuluk,et al.  A General Approach to Sampled-Data Modeling for Power Electronic Circuits , 1986, IEEE Transactions on Power Electronics.

[13]  Ali Emadi,et al.  Modeling of power electronic loads in AC distribution systems using the generalized State-space averaging method , 2004, IEEE Transactions on Industrial Electronics.

[14]  Jun Zhang,et al.  Pseudocoevolutionary genetic algorithms for power electronic circuits optimization , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Jun Zhang,et al.  Solving the flight frequency programming problem with particle swarm optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[16]  Jun Zhang,et al.  Discrete Particle Swarm Optimization for Multiple Destination Routing Problems , 2009, EvoWorkshops.

[17]  H.S.-H. Chung,et al.  Extended Ant Colony Optimization Algorithm for Power Electronic Circuit Design , 2009, IEEE Transactions on Power Electronics.

[18]  Jun Zhang,et al.  An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem , 2010, IEEE Transactions on Intelligent Transportation Systems.

[19]  L. P. Huelsman.  Optimization-a powerful tool for analysis and design , 1993 .

[20]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[21]  Jun Zhang,et al.  Power Electronic Circuits Design: A Particle Swarm Optimization Approach , 2008, SEAL.

[22]  George C. Verghese,et al.  Principles of power electronics , 1991 .

[23]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, ANTS Conference.

[24]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[25]  Michael N. Vrahatis,et al.  On the computation of all global minimizers through particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[26]  Jun Zhang,et al.  Solving the Optimal Coverage Problem in Wireless Sensor Networks Using Evolutionary Computation Algorithms , 2010, SEAL.

[27]  P. R. K. Chetty.  Current Injected Equivalent Circuit Approach to Modeling of Switching DC-DC Converters in Discontinuous Inductor Conduction Mode , 1982, IEEE Transactions on Industrial Electronics.

[28]  M. Young.  The technical writer's handbook : writing with style and clarity , 1989 .

[29]  Fred C. Lee,et al.  State-plane analysis of parallel resonant converter , 1985, 1985 IEEE Power Electronics Specialists Conference.