This paper proposes a new multi-objective genetic algorithm, called GAME, to solve constrained optimization problems. GAME uses an elitist archive, but it ranks the population in several Pareto fronts. Then, three types of fitness assignment methods are defined: the fitness of individuals depends on the front they belong to. The crowding distance is also used to preserve diversity. Selection is...

Given a first set of observations from a design of experiments sampled randomly in the design space, the corresponding set of non-dominated points usually does not give a good approximation of the Pareto front. We propose here to study this problem from the point of view of multivariate analysis, introducing a probabilistic framework with the use of copulas. This approach enables the expression...

In multi-objective optimization the hypervolume indicator is a measure for the size of the space within a reference set that is dominated by a set of μ points. It is a common performance indicator for judging the quality of Pareto front approximations. As it does not require a-priori knowledge of the Pareto front it can also be used in a straightforward manner for guiding the search for finite ...

Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms, the problem of selecting a way to distribute or guide these solutions in a high-dimensional space has not been explored. In this work, we introduc...

Many real-world problems have a multiobjective character. A-posteriori techniques such as multiobjective evolutionary algorithms (MOEA) generate best compromise solution sets, i.e. Pareto-fronts and Pareto-sets. Classic MOEA are able to find quite efficiently good approximations of the complete Pareto-fronts also for very complex problems. In real-world applications only small sections of the c...

Multidisciplinary Design Optimization (MDO) problems can have a unique objective or be multi-objective. In this paper, we are interested in MDO problems having at least two conflicting objectives. This characteristic ensures the existence of a set of compromise solutions called Pareto front. We treat those MDO problems like Multi-Objective Optimization (MOO) problems. Actual MOO methods suffer ...