Name
Abstract | ||
|---|---|---|
In 2005, Krishnanand and Ghose (Multimodal function optimization using a glowworm metaphor with applications to collective robotics, 2005a), presented the idea of glowworm metaphor to determine multiple minima in the optimization problem arising in robotics applications. That research paper highlights the glowworm swarm behavior for determining multiple local minima for multimodal functions with application to robotics. Since then, a number of research papers have appeared to improve the performance of glowworm swarm optimization (GSO). In this paper, two major contributions are made. Firstly, a mathematical result is proved which shows that the step size of GSO has a significant influence on the convergence of GSO. Secondly, three variants of GSO are proposed which depend on different step size. Based on the implementation of the proposed variants and the original GSO on 15 benchmark problems, it is concluded that one of the proposed variants is a definite improvement over the original GSO and the remaining variants. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s13198-015-0371-5 | Int. J. Systems Assurance Engineering and Management |
Keywords | Field | DocType |
Glowworm swarm optimization, Swarm intelligence, Nature inspired algorithm, Optimization techniques | Convergence (routing),Mathematical optimization,Swarm intelligence,Glowworm swarm optimization,Maxima and minima,Multi-swarm optimization,Artificial intelligence,Engineering,Optimization problem,Definite Improvement,Robotics | Journal |
Volume | Issue | ISSN |
6 | 3 | 0976-4348 |
Citations | PageRank | References |
3 | 0.38 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amarjeet Singh | 1 | 526 | 49.37 |
| Kusum Deep | 2 | 876 | 82.14 |