Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Using predators and preys in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Designing multi-objective variation operators using a predator-prey approach
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Adapting to the Habitat: On the Integration of Local Search into the Predator-Prey Model
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
| Hi-index | 0.00 |
In this paper, we apply the parallel predator-prey model for multi-objective optimization to a combinatorial problem for the first time: Exemplarily, we optimize sequences of 50 jobs for an instance of the bi-criteria scheduling problem 1|dj| 茂戮驴 Cj,Lmaxwith this approach. The modular building block architecture of the predator-prey system and the distribution of acting entities enables the analysis of separated problem knowledge and the design of corresponding variation operators. The actual modules are derived from local heuristics that tackle fractions of the complete problem. We unveil that it is possible to cover different areas of the Pareto-front with special property operators and make evident that the whole front can be covered if those operators are applied simultaneously to the spatial population. Further, we identify open problems that arise when the predator-prey model is applied to combinatorial problems which have not yet occurred for real-valued optimization problems.