Abstract
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.
Highlights
Most engineering optimal design problems require optimization techniques that are capable of handling the problem of equality and inequality constraints
Thereafter, the particles continue to evolve in the aforementioned manner and try to explore such a feasible region to find an optimal solution that satisfies the given constraint conditions
Our experimental results demonstrate that the proposed particle swarm optimization (PSO) strategy for handling mixed variables in optimization problems is more likely to find the global optimum solution with greater accuracy compared to existing methods
Summary
Most engineering optimal design problems require optimization techniques that are capable of handling the problem of equality and inequality constraints. The constrained-PSO-based approach with rounding-off technique tested in Kim et al [18] may not provide sufficient optimization reliability, in the sense that the probability of obtaining an optimal design variable is not sufficiently high Such a shortcoming may be due to the poor particle diversification characteristics which frequently causes the potential problem of premature convergence of the swarm to local optima in a practical implementation. Thereafter, the particles continue to evolve in the aforementioned manner and try to explore such a feasible region to find an optimal solution that satisfies the given constraint conditions Note that it may not be considered the best possible approach, the rounded-off technique has been used with promising results as demonstrated in many studies [2, 18, 24, 27, 28, 38,39,40, 48, 49]. We demonstrate the scheme’s distinctive features through several numerical examples
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