Interactive multiobjective optimization system WWW-NIMBUS on the internet
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
A Post-Optimality Analysis Algorithm for Multi-Objective Optimization
Computational Optimization and Applications
Multicriteria Optimization
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A multi-objective approach for the motion planning of redundant manipulators
Applied Soft Computing
A random search heuristic for a multi-objective production planning
Computers and Industrial Engineering
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Multi-objective optimization algorithms can generate large sets of Pareto optimal (non-dominated) solutions. Identifying the best solutions across a very large number of Pareto optimal solutions can be a challenge. Therefore it is useful for the decision-maker to be able to obtain a small set of preferred Pareto optimal solutions. This paper analyzes a discrete optimization problem introduced to obtain optimal subsets of solutions from large sets of Pareto optimal solutions. This discrete optimization problem is proven to be NP-hard. Two exact algorithms and five heuristics are presented to address this problem. Five test problems are used to compare the performances of these algorithms and heuristics. The results suggest that preferred subset of Pareto optimal solutions can be efficiently obtained using the heuristics, while for smaller problems, exact algorithms can be applied.