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Design and
analysis optimization methods use specialized algorithms to
iteratively search the design space for the best values of input
variables to achieve a specified goal. In many cases,
optimization can be applied to everyday design problems to
improve product performance/quality while simultaneously
reducing manufacturing cost, weight, etc.
Multidisciplinary Design Optimization (MDO) simply refers to the
use of optimization methods on system-level models. For
example, MDO methods can be used to minimize a product's
predicted cost, while satisfying engineering, manufacturing, and
logistics variables and constraints. There are special
methodologies that can be utilized to aid in exploration and
simplification of complex MDO problems, such as Design of
Experiments (DOE) and Response Surface Methodology (RSM).
Optimization algorithms that are used for engineering problems
can be grouped into two categories: deterministic and
stochastic.
Deterministic
methods include gradient-based and simplex algorithms. Gradient-based
algorithms
attempt to find an optimum by searching along the direction of
steepest descent of the objective function, until some
convergence criterion is met. Some gradient-based methods are
designed to handle constraints. If used properly on the right kinds of
problems, gradient-based algorithms usually require the fewest evaluations
to find a solution. However, these
algorithms are susceptible to finding a
local (rather than global) optimum if a poor start point is
selected. Also, gradient-based methods are ill-suited for problems
with discrete variables or noisy objective functions.
Stochastic
methods include genetic algorithms, simulated annealing, Monte
Carlo, and random walk. All of these algorithms employ
probabilistic methodologies (randomness) in their search. Due to
the probabilistic nature of stochastic methods, it is often
possible to parallelize evaluations of the objective function in
order to speed up the solution. This is a major advantage
over gradient-based optimization, which is impossible to
parallelize.
Genetic algorithms,
for example, are very useful for
engineering problems with multiple optima, a noisy objective
function, multiple objectives, and/or discrete design variables.
Genetic algorithms start with a more global search and
eventually converge to an optimal population of designs.
However, they have the disadvantage of requiring more
evaluations to converge, compared to gradient-based methods.
In addition, many genetic algorithm implementations do not
handle constraints.
Find out more about TechnoSoft's
design optimization capabilities:
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