In this paper we propose a new algorithm for solving multiobjective optimization problems. Our proposal adapts the well-known scatter search template for single objective optimization to the multiobjective domain. The result is a hybrid metaheuristic algorithm called AbYSS, which follows the scatter search structure but using mutation and crossover operators coming from the field of evolutionar...

A significant challenge to the application of evolutionary multiobjective optimization (EMO) for transonic airfoil design is the often excessive number of computational fluid dynamic (CFD) simulations required to ensure convergence. In this study, a multiobjective particle swarm optimization (MOPSO) framework is introduced, which incorporates designer preferences to provide further guidance in ...

To get positive Lagrange multipliers associated with each of the objective function, Maeda [Constraint qualification in multiobjective optimization problems: Differentiable case, J. Optimization Theory Appl., 80, 483–500 (1994)], gave some special sets and derived some generalized regularity conditions for first-order Karush–Kuhn– Tucker (KKT)-type necessary conditions of multiobjective optimiz...

Multiobjective optimization deals with problems involving multiple measures of performance that should be optimized simultaneously. In this paper we extend bucket elimination (BE), a well known dynamic programming generic algorithm, from mono-objective to multiobjective optimization. We show that the resulting algorithm, MO-BE, can be applied to true multi-objective problems as well as mono-obj...

This paper presents a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO. A concise overview of evolutionary algorithms for multiobjective optimization is given. A substantial number of methods has been proposed so far, and an attempt of conceptually unifying existing approaches is presented he...

The No-Free-Lunch (NFL) theorems hold for general multiobjective fitness spaces, in the sense that, over a space of problems which is closed under permutation, any two algorithms will produce the same set of multiobjective samples. However, there are salient ways in which NFL does not generally hold in multiobjective optimization. Previously we have shown that a ‘free lunch’ can arise when comp...