Paper Info
Title
Efficient micro immune optimization approach solving constrained nonlinear interval number programming
Abstract
This work investigates a possibility degree-based micro immune optimization approach to seek the optimal solution of nonlinear interval number programming with constraints. Such approach is designed under the guideline of the theoretical results acquired in the current work, relying upon interval arithmetic rules, interval order relation and immune theory. It involves in two phases of optimization. The first phase, based on a new possibility degree approach, assumes searching efficient solutions of natural interval extension optimization. This executes five modules - constraint bound handling, population division, dynamic proliferation, mutation and selection, with the help of a varying threshold of interval bound. The second phase collects the optimal solution(s) from these efficient solutions after optimizing the bounds of their objective intervals, in terms of the theoretical results. The numerical experiments illustrated that such approach with high efficiency performs well over one recent nested genetic algorithm and is of potential use for complex interval number programming.
Year
DOI
Venue
2015
10.1007/s10489-014-0639-5
Applied Intelligence
Keywords
Field
DocType
Nonlinear programming,Interval number,Interval analysis,Optimal-valued interval,Immune optimization
Population,Mathematical optimization,Interval order,Nonlinear system,Computer science,Nonlinear programming,Interval arithmetic,Genetic algorithm
Journal
Volume
Issue
ISSN
43
2
0924-669X
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
Zhuhong Zhang118616.41
Juan Tao200.68