Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Soviet multi-objective mathematical programming methods: an overview
Management Science
A numerical model of a high performance two-stroke engine
Applied Numerical Mathematics
Principles of data mining
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Post-Optimality Analysis Algorithm for Multi-Objective Optimization
Computational Optimization and Applications
Clustering and classification based on the L1data depth
Journal of Multivariate Analysis
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A Highly Efficient Multi-objective Optimization Evolutionary Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A preference-based evolutionary algorithm for multi-objective optimization
Evolutionary Computation
The smart normal constraint method for directly generating a smart Pareto set
Structural and Multidisciplinary Optimization
Hi-index | 0.00 |
Typically, industrial optimization problems need to be solved in an efficient, multiobjective and global manner, because they are often computationally expensive (as function values are typically based on simulations), they may contain multiple conflicting objectives, and they may have several local optima. Solving such problems may be challenging and time consuming when the aim is to find the most preferred Pareto optimal solution. In this study, we propose a method where we use an advanced clustering technique to reveal essential characteristics of the approximation of the Pareto optimal set, which has been generated beforehand. Thus, the decision maker (DM) is involved only after the most time consuming computation is finished. After the initiation phase, a moderate number of cluster prototypes projected to the Pareto optimal set is presented to the DM to be studied. This allows him/her to rapidly gain an overall understanding of the main characteristics of the problem without placing too much cognitive load on the DM. Furthermore, we also suggest some ways of applying our approach to different types of problems and demonstrate it with an example related to internal combustion engine design.