Multiple objective design allocation problems: Development of new evolutionary algorithms

This paper describes the use of multiple objective evolutionary algorithms to solve engineering design allocation problems. The first algorithm presented is MOEA-DAP, a multiobjective evolutionary algorithm for solving design allocation problems. In MOEA-DAP, the multiobjective formulation considered was to maximize system reliability, minimize the total cost, and minimize the system weight, for a series-parallel system. However, this algorithm considers binary-state reliability, that is, the system and its components can be in either a completely working or a completely failed state only. MOMS-GA, the second algorithm, is an extension of MOEA-DAR MOMS-GA is an algorithm that allows the user to solve multiobjective multi-state design allocation problems. That is, MOMS-GA works under the assumption that both the system and its components can experience more than two possible states of performance. MOMS-GA uses the universal moment generating function (UMGF) approach to evaluate the different reliability indices of the system. The two algorithms developed have the strength of a problem-oriented technique. Two illustrative examples are presented to show the performance of the evolutionary algorithms developed.