Name
Abstract | ||
|---|---|---|
Bacterial foraging optimization (BFO) is predominately used to find solutions for real-world problems. One of the major characteristics of BFO is the chemotactic movement of a virtual bacterium that models a trial solution of the problems. It is pointed out that the chemotaxis employed by classical BFO usually results in sustained oscillation, especially on rough fitness landscapes, when a bacterium cell is close to the optima. In this paper we propose a novel adaptive computational chemotaxis based on the concept of field, in order to accelerate the convergence speed of the group of bacteria near the tolerance. Firstly, a simple scheme is designed for adapting the chemotactic step size of each field. Then, the scheme chooses the fields which perform better to boost further the convergence speed. Empirical simulations over several numerical benchmarks demonstrate that BFO with adaptive chemotactic operators based on field has better convergence behavior, as compared against other meta-heuristic algorithms. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s00500-013-1089-4 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
swam intelligence,field,index terms-bacterial foraging,global optimization,computational chemotaxis | Convergence (routing),Chemotaxis,Mathematical optimization,Fitness landscape,Global optimization,Computer science,Operator (computer programming),Foraging | Journal |
Volume | Issue | ISSN |
18 | 4 | 1433-7479 |
Citations | PageRank | References |
23 | 0.69 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Xu | 1 | 23 | 0.69 |
| Hui-Ling Chen | 2 | 109 | 5.77 |