Name
Title | ||
|---|---|---|
An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem. |
Abstract | ||
|---|---|---|
Research on multi-objective optimization (MO) has become one of the hot points of intelligent computation. In this paper, an archive-based multi-objective artificial bee colony optimization algorithm (AMOABC) is proposed, in which an external archive is used to preserve the current obtained non-dominated best solutions, and a novel Pareto local search mechanism is designed and incorporated into the optimization process. To prevent the searching process from being trapped into local minimum, a novel food source generating mechanism is put forward, and different search strategies are designed for bees and local search process. Comprehensive benchmarking and comparison of AMOABC with the some current-related MO algorithms demonstrate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00521-016-2821-7 | Neural Computing and Applications |
Keywords | Field | DocType |
Multi-objective optimization, Artificial bee colony, Food source, Swarm intelligence | Swarm intelligence,Multi-objective optimization,Artificial intelligence,Metaheuristic,Artificial bee colony algorithm,Mathematical optimization,Meta-optimization,Algorithm,Multi-swarm optimization,Bees algorithm,Local search (optimization),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
30 | 9 | 0941-0643 |
Citations | PageRank | References |
1 | 0.36 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiaxu Ning | 1 | 141 | 9.00 |
| Bin Zhang | 2 | 213 | 41.40 |
| Tingting Liu | 3 | 4 | 1.43 |
| Changsheng Zhang | 4 | 199 | 15.90 |