This chapter deals with the application of hybrid evolutionary methods to design optimization issues in which approximation techniques and model management strategies can be used to guide the decision making process in a multidisciplinary context. An enhanced evolutionary algorithmic scheme devoted to design optimization is proposed, and its use in real applications is illustrated in the framework of the multidisciplinary design optimization (MDO). At this aim, a case study is discussed. It relies to the field of automotive engineering in which the design optimization of a system is carried out considering simultaneously both mechanical and control requirements. The studied system is the regulator of the injection pressure of an innovative common rail system for compressed natural gas (CNG) engines, whose engineering design optimization includes several practical and numerical difficulties. To tackle such a situation, a multiobjective optimization formulation of the problem is proposed. The adopted optimization strategy pursues the Pareto optimality on the basis of fitness functions that capture domain specific design aspects as well as static and dynamic objectives. The computational experiments show the ability of the proposed method for finding a satisfactory set of efficient solutions.
|Titolo:||Enhanced evolutionary algorithms for Multidisciplinary Design Optimization: a control engineering perspective|
|Titolo del libro:||Hybrid Evolutionary Algorithms|
|Data di pubblicazione:||2007|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-540-73297-6_3|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|