Name
Abstract | ||
|---|---|---|
The EigenAnt, an algorithm for Ant Colony Optimization with convergence guarantees, has been shown to be suitable for continuous global optimization. Conventionally, the Multi-Trajectory Local Search (Mtsls1) algorithm was used for efficient local search, and it searches along function dimensions sequentially. On the other hand, EigenAnt provides an approach to select dimensions where higher gain in terms of function value is obtained. The Modified Mtsls1 (MMtsls1) approach improved the performance of the original Mtsls1 algorithm by implementing a strategy which requires fewer number of function evaluations during the local search procedure. This paper presents an approach called the EigenAnt Modified Mtsls1 (Eigen-MM), which combines the advantages of the Modified Mtsls1 with EigenAnt to better optimize benchmark functions by using fewer number of function evaluations. Our results demonstrate that the Eigen-MM approach is particularly effective for high-dimensional functions. On a number of selected benchmarks, we obtain upto 88.2% reduction in the number of function evaluations. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.swevo.2018.04.014 | Swarm and Evolutionary Computation |
Keywords | Field | DocType |
Incremental ant colony optimization,Continuous global optimization,Local search,Multi-Trajectory Local Search,EigenAnt | Convergence (routing),Ant colony optimization algorithms,Mathematical optimization,Global optimization,Computer science,Local search (optimization) | Journal |
Volume | ISSN | Citations |
43 | 2210-6502 | 0 |
PageRank | References | Authors |
0.34 | 41 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Udit Kumar | 1 | 1 | 1.36 |
| sumit soman | 2 | 20 | 7.53 |
| jayadeva | 3 | 67 | 10.50 |