Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multiobjective hBOA, clustering, and scalability
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Approximating the Knee of an MOP with Stochastic Search Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Optimization of scalarizing functions through evolutionary multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
A multi-objective approach for the motion planning of redundant manipulators
Applied Soft Computing
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The main characteristic feature of evolutionary multiobjective optimization (EMO) is that no a priori information about the decision maker's preference is utilized in the search phase. EMO algorithms try to find a set of well-distributed Pareto-optimal solutions with a wide range of objective values. It is, however, very difficult for EMO algorithms to find a good solution set of a multiobjective combinatorial optimization problem with many decision variables and/or many objectives. In this paper, we propose an idea of incorporating the decision maker's preference into EMO algorithms to efficiently search for Pareto-optimal solutions of such a hard multiobjective optimization problem.