- Tytuł:
- Recognizing Sets in Evolutionary Multiobjective Optimization
- Autorzy:
- Gajda-Zagórska, E.
- Powiązania:
- https://bibliotekanauki.pl/articles/308467.pdf
- Data publikacji:
- 2012
- Wydawca:
- Instytut Łączności - Państwowy Instytut Badawczy
- Tematy:
-
basin of attraction
clustering
genetic algorithm
multiobjective optimization - Opis:
- Among Evolutionary Multiobjective Optimization Algorithms (EMOA) there are many which find only Paretooptimal solutions. These may not be enough in case of multimodal problems and non-connected Pareto fronts, where more information about the shape of the landscape is required. We propose a Multiobjective Clustered Evolutionary Strategy (MCES) which combines a hierarchic genetic algorithm consisting of multiple populations with EMOA rank selection. In the next stage, the genetic sample is clustered to recognize regions with high density of individuals. These regions are occupied by solutions from the neighborhood of the Pareto set. We discuss genetic algorithms with heuristic and the concept of well-tuning which allows for theoretical verification of the presented strategy. Numerical results begin with one example of clustering in a single-objective benchmark problem. Afterwards, we give an illustration of the EMOA rank selection in a simple two-criteria minimization problem and provide results of the simulation of MCES for multimodal, multi-connected example. The strategy copes with multimodal problems without losing local solutions and gives better insight into the shape of the evolutionary landscape. What is more, the stability of solutions in MCES may be analyzed analytically.
- Źródło:
-
Journal of Telecommunications and Information Technology; 2012, 1; 74-82
1509-4553
1899-8852 - Pojawia się w:
- Journal of Telecommunications and Information Technology
- Dostawca treści:
- Biblioteka Nauki
Artykuł