SIAM Journal on Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Generating the weakly efficient set of nonconvex multiobjective problems
Journal of Global Optimization
Multiobjective Optimization Through a Series of Single-Objective Formulations
SIAM Journal on Optimization
An Adaptive Scalarization Method in Multiobjective Optimization
SIAM Journal on Optimization
Scalarizations for adaptively solving multi-objective optimization problems
Computational Optimization and Applications
New quality measures for multiobjective programming
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper, a new numerical method is presented for constructing an approximation of the Pareto front of multi-objective optimization problems. This method is based on the well-known scalarization approach by Pascoletti and Serafini. The proposed method is applied to four test problems that illustrate specific difficulties encountered in multi-objective optimization problems, such as nonconvex, disjoint and local Pareto fronts. The effectiveness of the proposed method is demonstrated by comparing it with the NSGA-II algorithm and the Normal Constraint method.