Name
Abstract | ||
|---|---|---|
Artificial Bee Colony algorithm (ABC) is a nature inspired heuristics optimization methodology which is competitive to other population-based stochastic algorithms. Recent studies have shown that ABC is good in exploration but poor in exploitation. In this paper, we have applied co-variance matrix adoption and chaotic map in order to improve the convergence toward the solution of ABC algorithm. Proposed chaotic and covariance-based ABC (C2ABC) gives a better result than ABC and it's variants most of the time when applied to Black-Box Optimization Benchmarking (BBOB). Our literature proves that proposed C2ABC is better than most of the variants of ABC for continuous global optimization problem. |
Year | Venue | Keywords |
|---|---|---|
2020 | JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING | Swarm intelligence,artificial bee colony (ABC),chaotic map,co-variance,opposition-based learning |
DocType | Volume | Issue |
Journal | 34 | 1-2 |
ISSN | Citations | PageRank |
1542-3980 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| shashank gupta | 1 | 60 | 11.35 |
| Divya Kumar | 2 | 19 | 3.32 |
| K. K. Mishra | 3 | 0 | 0.34 |