Name
Abstract | ||
|---|---|---|
The nature-/bio-/socio-inspired optimisation techniques can efficiently handle unconstrained problems; however, their performance gets significantly affected when applied for solving constrained problems. This paper proposes a variation of the distributed optimisation multi-agent system (MAS) approach of probability collectives (PC) in collective intelligence domain referred to as multi-criteria probability collective (MCPC). In this approach, the constraints are efficiently handled by giving equal importance as the objective function. It is validated by solving a variety of constrained test problems including tension/compression spring design problem and pressure vessel design problem. The solution to these problems proves that the MCPC approach can be applied to a variety of complex practical/real world problems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1504/IJBIC.2014.066975 | International Journal of Bio-Inspired Computation |
Keywords | Field | DocType |
collective intelligence,multi agent system,coin | Mathematical optimization,Computer science,Collective intelligence,Multi-agent system,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 6 | 1758-0366 |
Citations | PageRank | References |
1 | 0.37 | 28 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neha S. Patankar | 1 | 3 | 1.08 |
| Anand Jayant Kulkarni | 2 | 19 | 2.48 |
| K. Tai | 3 | 177 | 22.25 |
| T. D. Ghate | 4 | 1 | 0.37 |
| A. R. Parvate | 5 | 1 | 0.37 |