Name
Abstract | ||
|---|---|---|
Real-world optimization problems typically include uncertainties over various aspects of the problem formulation. Some existing algorithms are designed to cope with stochastic multiobjective optimization problems, but in order to benchmark them, a proper framework still needs to be established. This paper presents a novel toolkit that generates scalable, stochastic, multiobjective optimization problems. A stochastic problem is generated by transforming the objective vectors of a given deterministic test problem into random vectors. All random objective vectors are bounded by the feasible objective space, defined by the deterministic problem. Therefore, the global solution for the deterministic problem can also serve as a reference for the stochastic problem. A simple parametric distribution for the random objective vector is defined in a radial coordinate system, allowing for direct control over the dual challenges of convergence towards the true Pareto front and diversity across the front. An example for a stochastic test problem, generated by the toolkit, is provided. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2908812.2908873 | GECCO |
Keywords | Field | DocType |
robust optimization, benchmark problems, multiobjective optimization | Stochastic optimization,Mathematical optimization,Computer science,Robust optimization,Stochastic neural network,Multi-objective optimization,Parametric statistics,Artificial intelligence,Stochastic programming,Optimization problem,Machine learning,Bounded function | Conference |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shaul Salomon | 1 | 20 | 4.44 |
| Robin C. Purshouse | 2 | 628 | 30.00 |
| Ioannis Giaghiozis | 3 | 0 | 0.34 |
| Peter J. Fleming | 4 | 3023 | 475.23 |