Statistical independence properties of pseudorandom vectors produced by matrix generators
Journal of Computational and Applied Mathematics - Random numbers and simulation
Use of P&tgr;-nets for the approximation of the Edgeworth-Pareto set in multicriteria optimization
Journal of Optimization Theory and Applications
Multi-Objective Programming in the U. S. S. R.
Multi-Objective Programming in the U. S. S. R.
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Multi-criteria approach in configuration of energy efficient sensor networks
Proceedings of the 43rd annual Southeast regional conference - Volume 2
| Hi-index | 0.09 |
Applied optimization problems such as design, identification, design of controlled systems, operational development of prototypes, analysis of large-scale systems, and forecasting from observational data are multicriteria problems in essence. Construction of the feasible solution set is of primary importance in the above problems. The definition of a feasible solution set is usually considered to be the skill of a designer. Even though this skill is essential, it is by no means sufficient for the correct statement of the problem. There are many antagonistic performance criteria and all kinds of constraints in these problems; therefore, it is quite difficult to correctly determine the feasible set. As a result, ill-posed problems are solved, and optimal solutions are searched for far from where they should be. As a consequence, the optimization results have no practical meaning. In this work we propose methods and tools that will assist the designer in defining the feasible solution set correctly.