Name
Abstract | ||
|---|---|---|
Natural problems are not static but dynamic in nature. Dynamism could be due to a large number of factors such as inability to gauge accurate objective functions or changing constraints in a random environment. As such, dynamic optimization problems are an important requirement for solving a number of real life optimization problems. In this paper, we present a novel technique that not only detects the changing environment but also repositions itself intelligently in the changed landscape. Our algorithm is parallel and works on top of distributed peer-to-peer network in a complete asynchrony. We simulate our results, in a distributed environment ranging from 10 to 100 machines and show the scalability to be able to increase to thousands of nodes if required. The algorithm is tested on a popular set of benchmark functions and we show/compare the results with known solutions of such types. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-03844-5_46 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Particle swarm optimization,Decision variables,Mathematical optimization,Peer-to-peer,Computer science,Multi-swarm optimization,Solution set,Optimization problem,Particle swarm optimizer | Conference | 8297 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hrishikesh Dewan | 1 | 13 | 3.72 |
| Raksha B. Nayak | 2 | 0 | 1.69 |
| V. Susheela Devi | 3 | 47 | 9.21 |