Open AccessA Diversity-Enhanced Constrained Particle Swarm Optimizer for Mixed Integer-Discrete-Continuous Engineering Design Problems
Author(s) -
Semin Chun,
Young-Tark Kim,
TaeHyoung Kim
Publication year - 2013
Publication title -
advances in mechanical engineering/advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1155/2013/130750
Subject(s) - mathematical optimization , particle swarm optimization , integer (computer science) , continuous optimization , multi swarm optimization , discrete optimization , penalty method , optimization problem , simple (philosophy) , scheme (mathematics) , computer science , metaheuristic , integer programming , mathematics , programming language , mathematical analysis , philosophy , epistemology
Engineering optimization problems usually contain various constraints and mixed integer-discrete-continuous types of design variables. We propose an efficient particle swarm optimization (PSO) algorithm for such problems. First, we transform the constrained optimization problem into an unconstrained problem without introducing problem-dependent or user-defined parameters such as penalty factors or Lagrange multipliers (such parameters are usually required in general optimization algorithms). Then, we extend the above PSO method to handle integer, discrete, and continuous design variables in a simple manner with a high degree of precision. The proposed PSO scheme is fairly simple and therefore easy to implement. To demonstrate the effectiveness of our method, several mechanical design optimization problems are solved, and the numerical results are compared with results reported in the literature.