The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Fuzziness in Ant Colony Optimization and Their Applications
Abstract
Nature-inspired algorithms are still at a very early stage with a relatively short history, comparing with many traditional, well-established methods. Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. One major algorithm is Ant Colony Optimization which has been applied in varied domains to better the performance. Fuzzy Linear Programming models and methods has been one of the most and well-studied topics inside the broad area of Soft Computing. Its applications as well as practical realizations can be found in all the real-world areas. Here we wish to introduce how fuzziness can be included in a nature inspired algorithm like ant colony optimization and thereby enhance its functionality. Several applications of ACO with fuzzy concepts will be introduced in the chapter.
Related Content
|
P. Chitra, A. Saleem Raja, V. Sivakumar.
© 2024.
24 pages.
|
|
K. Ezhilarasan, K. Somasundaram, T. Kalaiselvi, Praveenkumar Somasundaram, S. Karthigai Selvi, A. Jeevarekha.
© 2024.
36 pages.
|
|
Kande Archana, V. Kamakshi Prasad, M. Ashok.
© 2024.
17 pages.
|
|
Ritesh Kumar Jain, Kamal Kant Hiran.
© 2024.
23 pages.
|
|
U. Vignesh, R. Elakya.
© 2024.
13 pages.
|
|
S. Karthigai Selvi, R. Siva Shankar, K. Ezhilarasan.
© 2024.
16 pages.
|
|
Vemasani Varshini, Maheswari Raja, Sharath Kumar Jagannathan.
© 2024.
20 pages.
|
|
|