Abstract
Engineering design of complex systems is a decision making process that aims at choosing from among a set of options that implies an irrevocable allocation of resources. It is inherently a multidisciplinary and multi- objective process; nowadays, the designer has to face the continuous growing complexity of engineering problems, but also, the increasing economic competition that have led to a specialization and distribution of knowledge, expertise, tools and work sites. Products become more and more complex and their design is usually of large scale, characterized by an important number of design variables, parameters, requirements, constraints and objectives. Consequently, multi- objective optimization (MOO) and multidisciplinary design optimization (MDO) are more and more used to provide one optimal solution—by the use of a “a priori” or interactive preferences modelling—or a set of optimal solutions—in which the designer will have to choose “a posteriori” the one to be developed. The objective of this paper is to present an original and efficient Collaborative Optimization Strategy for Multi-Objective Systems (COSMOS) designed to find the sub-set of the design space that contains the best solutions – Pareto frontier – of the global system.
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