Recent developed evolutionary algorithms for the multi-objective optimization of design allocation problems
Abstract
This paper presents an overview of a collection of recent developed evolutionary algorithms for solving different types of allocation problems under the consideration of several conflicting objectives. These algorithms are: MOEA-DAP, MOMS-GA and the MultiTask Multi-State MOEA. MOEA-DAP is a custom multiple objective evolutionary algorithm for solving design allocation problems. MOEA-DAP considers binarystate reliability. In contrast, MOMS-GA, which is a natural extension of MOEA-DAP, works under the assumption that both, the system and its components, experience more than two possible states of performance. The last algorithm presented in the paper is the MultiTask Multi-State MOEA, which is a multiple objective algorithm designed to determine optimal configurations of multi-state, multi-task production systems based on availability analysis. These three algorithms are novel approaches that offer distinct advantages to current existing MOEAs.