Cm. Fonseca et Pj. Fleming, MULTIOBJECTIVE OPTIMIZATION AND MULTIPLE CONSTRAINT HANDLING WITH EVOLUTIONARY ALGORITHMS - PART II - APPLICATION EXAMPLE, IEEE transactions on systems, man and cybernetics. Part A. Systems and humans, 28(1), 1998, pp. 38-47
The evolutionary approach to multiple function optimization formulated
in the first part of the paper [1] is applied to the optimization of
the low-pressure spool speed governor of a Pegasus gas turbine engine,
This study illustrates how a technique such as the multiobjective gen
etic algorithm can be applied, and exemplifies how design requirements
can be refined as the algorithm runs, Several objective functions and
associated goals express design concerns in direct form, i,e,, as the
designer would state them, While such a designer-oriented formulation
is very attractive, its practical usefulness depends heavily on the a
bility to search and optimize cost surfaces in a class much broader th
an usual, as already provided to a large extent by the genetic algorit
hm (GA), The two instances of the problem studied demonstrate the need
for preference articulation in cases where many and highly competing
objectives lead to a nondominated set too large for a finite populatio
n to sample effectively, It is shown that only a very small portion of
the nondominated set is of practical relevance, which further substan
tiates the need to supply preference information to the GA, The paper
concludes with a discussion of the results.