When attempting to optimize the design of engineered systems, the analyst i
s frequently faced with the demand of achieving several targets (e.g. low c
osts, high revenues, high reliability, low accident risks), some of which m
ay very well be in conflict. At the same time, several requirements (e.g. m
aximum allowable weight, volume etc.) should also be satisfied. This kind o
f problem is usually tacked by focusing the optimization on a single object
ive which may be a weighed combination of some of the targets of the design
problem and imposing some constraints to satisfy the other targets and req
uirements. This approach. however, introduces a strong arbitrariness in the
definition of the weights and constraints levels and a criticizable homoge
nization of physically different targets, usually all translated in monetar
y terms.
The purpose of this paper is to present an approach to optimization in whic
h every target is considered as a separate objective to be optimized. For a
n efficient search through the solution space we use a multiobjective genet
ic algorithm which allows us to identify a set of Pareto optimal solutions
providing the decision maker with the complete spectrum of optimal solution
s with respect to the various targets. Based on this information, the decis
ion maker can select the best compromise among these objectives, without a
priori introducing arbitrary weights. (C) 2001 Elsevier Science Ltd. All ri
ghts reserved.