The increasing economic competition of all industrial markets and growing complexity of engineering problems lead to a progressive specialization and distribution of expertise, tools and works. On the other hand, engineering products becomes more and more complex and the designer has to face with an increase design variables and design objectives. Besides multi-objective optimization (MOO) and multi-disciplinary design optimization (MDO) are more commonly used as methods to provide optimal solutions for complex design problems. The paper describes an innovative mixing between genetic algorithms (MOGA) and collaborative optimization (CO) as a tool to: 1) increase the convergence rate when a design problem can be broken up regarding design variables, and 2) provide an optimal set of design variables in case of multi-level design problem. This method gives multidisciplinary optimization the advantages AG has brought to multi-objective optimization. The method, tested on test functions, assures high optimization results containing CPU times.

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