Name
Abstract | ||
|---|---|---|
The Bacterial Foraging Optimization (BFO) is one of the metaheuristics algorithms that most widely used to solve optimization problems. The BFO is imitated from the behavior of the foraging bacteria group such as Ecoli. The main aim of algorithm is to eliminate those bacteria that have weak foraging methods and maintaining those bacteria that have strong foraging methods. In this extent, each bacterium communicates with other bacteria by sending signals such that bacterium change the position in the next step if prior factors have been satisfied. In fact, the process of algorithm allows bacteria to follow up nutrients toward the optimal. In this paper, the BFO is used for the solutions of Quadratic Assignment Problem (QAP), and multi- objective QAP (mQAP) by using updating mechanisms including mutation, crossover, and a local search. |
Year | Venue | DocType |
|---|---|---|
2016 | arXiv: Neural and Evolutionary Computing | Journal |
Volume | Citations | PageRank |
abs/1606.04055 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeid Parvandeh | 1 | 0 | 0.68 |
| Ahmet Ünveren | 2 | 35 | 6.53 |
| Bill C. White | 3 | 20 | 3.09 |
| Mohammadreza Boroumand | 4 | 0 | 0.34 |
| Parya Soltani | 5 | 0 | 0.34 |