Name
Abstract | ||
|---|---|---|
This paper proposes a novel migration strategy for Self-organizing Migrating Algorithm (SOMA), which combines advantages of the explorative All-To-Random migration with new exploitation focused All-To-Cluster-Leaders strategy. The main goal of this novel innovation to SOMA is to deliver competitive results, not only on the latest CEC 2020 benchmark set on a single objective bound-constrained numerical optimization. The proposed algorithm variant was titled SOMA-CL, and it has manifested notable potential in such demanding challenges. The results of the proposed algorithm were compared and tested for statistical significance against two other SOMA variants.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3377929.3398129 | GECCO '20: Genetic and Evolutionary Computation Conference
Cancún
Mexico
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7127-8 | 2 |
PageRank | References | Authors |
0.42 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomas Kadavy | 1 | 20 | 20.97 |
| Michal Pluhacek | 2 | 217 | 47.34 |
| Adam Viktorin | 3 | 5 | 8.23 |
| Roman Senkerik | 4 | 375 | 74.92 |