Nephron Algorithm Optimization: Inspired of the Biologic Nephron Performance

Reza Behmanesh
Additional contact information
Reza Behmanesh: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

International Journal of Applied Metaheuristic Computing (IJAMC), 2016, vol. 7, issue 1, 38-64

Abstract: A new Meta heuristic algorithm inspired of the biologic nephron performance for optimization of objective functions in Np-hard problems is introduced. The complexity of the problems increases with their size, and hence their solution space increases exponentially. Despite of designing the several search techniques with balanced exploration and exploitation in order to solve such as these problems, there are some drawbacks to make suitable adjustment between exploring and exploiting in performance of the Meta heuristic algorithms. The proposed algorithm in this paper can adjust between intensification and diversification strategies intrinsically, to make efficient optimization technique. For testing Nephron algorithm optimization (NAO), the traveling salesman problem (TSP) is provided as a solution in various sizes. Results indicate that NAO provides robust optimal solutions.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2016010103 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:7:y:2016:i:1:p:38-64

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().