Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Metaheuristics: computer decision-making
Metaheuristics: computer decision-making
Information Sciences: an International Journal
Fuzzy logic based algorithms for maximum covering location problems
Information Sciences: an International Journal
A novel global optimization technique for high dimensional functions
International Journal of Intelligent Systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
Hybrid line search for multiobjective optimization
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Information Sciences: an International Journal
A note on continuity in scalarization for multicriteria optimization problems
Information Sciences: an International Journal
A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection
Information Sciences: an International Journal
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The aggregation of objectives in multiple criteria programming is one of the simplest and widely used approach. But it is well known that this technique sometimes fail in different aspects for determining the Pareto frontier. This paper proposes a new approach for multicriteria optimization, which aggregates the objective functions and uses a line search method in order to locate an approximate efficient point. Once the first Pareto solution is obtained, a simplified version of the former one is used in the context of Pareto dominance to obtain a set of efficient points, which will assure a thorough distribution of solutions on the Pareto frontier. In the current form, the proposed technique is well suitable for problems having multiple objectives (it is not limited to bi-objective problems) and require the functions to be continuous twice differentiable. In order to assess the effectiveness of this approach, some experiments were performed and compared with two recent well known population-based metaheuristics namely ParEGO and NSGA II. When compared to ParEGO and NSGA II, the proposed approach not only assures a better convergence to the Pareto frontier but also illustrates a good distribution of solutions. From a computational point of view, both stages of the line search converge within a short time (average about 150ms for the first stage and about 20ms for the second stage). Apart from this, the proposed technique is very simple, easy to implement and use to solve multiobjective problems.