NEW APPROACH TO CONSTRAINED SHAPE OPTIMIZATION USING GENETIC ALGORITHMS

A robust genetic algorithm for constrained functional optimization is described, The function being sought is represented both in a piecewis e-linear fashion and in two different types of orthogonal series repre sentations, satisfying in each case specified end conditions of both D irichlet and Neumann types. The search for the optimal function is tra nslated to one of determining the coefficients of a series expansion, and a genetic algorithm is developed for this purpose, The method is v alidated in terms of test problems for which the global optimum soluti ons are known, The results indicate that, if the population size of th e chromosome pool is held constant, the performance of the piecewise-l inear-representation approach deteriorates considerably as the number of degrees of freedom increases, In contrast, the orthogonal series re presentations do not suffer from this drawback, and a significant redu ction in the population size can be achieved. Therefore, the latter me thodology offers a far more efficient approach to functional optimizat ion than previously attempted, The developed methodology was applied t o the determination of an optimal micropump shape. The genetic algorit hm uncovered shapes that were nonintuitive but yielded vastly superior pump performance.