Multiobjective optimization by genetic algorithms: application to safety systems

Citation
Pg. Busacca et al., Multiobjective optimization by genetic algorithms: application to safety systems, RELIAB ENG, 72(1), 2001, pp. 59-74
Citations number
28
Categorie Soggetti
Engineering Management /General
Journal title
RELIABILITY ENGINEERING & SYSTEM SAFETY
ISSN journal
09518320 → ACNP
Volume
72
Issue
1
Year of publication
2001
Pages
59 - 74
Database
ISI
SICI code
0951-8320(200104)72:1<59:MOBGAA>2.0.ZU;2-Y
Abstract
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.