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Nephron Algorithm Optimization: Inspired of the Biologic Nephron Performance

Nephron Algorithm Optimization: Inspired of the Biologic Nephron Performance

Reza Behmanesh
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 27
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781466691209|DOI: 10.4018/IJAMC.2016010103
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MLA

Behmanesh, Reza. "Nephron Algorithm Optimization: Inspired of the Biologic Nephron Performance." IJAMC vol.7, no.1 2016: pp.38-64. http://doi.org/10.4018/IJAMC.2016010103

APA

Behmanesh, R. (2016). Nephron Algorithm Optimization: Inspired of the Biologic Nephron Performance. International Journal of Applied Metaheuristic Computing (IJAMC), 7(1), 38-64. http://doi.org/10.4018/IJAMC.2016010103

Chicago

Behmanesh, Reza. "Nephron Algorithm Optimization: Inspired of the Biologic Nephron Performance," International Journal of Applied Metaheuristic Computing (IJAMC) 7, no.1: 38-64. http://doi.org/10.4018/IJAMC.2016010103

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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.

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