Swarm intelligence for mixed-variable design optimization

INTRODUCTION Most nonlinear optimization methods assumethat objective function variables are continuous.However, many practical engineering designproblems frequently encounter discrete variables aswell as continuous variables. Discrete variables areused in many ways such as the representation of theset of standard sized components, the decision onthe number of identical parts or the choice betweendifferent design options. For example, the numberof the teeth of a gear must be chosen …

Swarm intelligence for mixed-variable design optimization

Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap- proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.