AITSO: A Tool for Spatial Optimization Based on Artificial Immune Systems

被引:2
作者
Zhao, Xiang [1 ]
Liu, Yaolin [1 ,2 ]
Liu, Dianfeng [1 ]
Ma, Xiaoya [1 ]
机构
[1] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China
[2] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China
基金
美国国家科学基金会;
关键词
PARTICLE SWARM OPTIMIZATION; CLONAL SELECTION; CONCEPTUAL-FRAMEWORK; MONITORING NETWORK; GIS; ALLOCATION; ALGORITHM; AREA; SIMULATION; DYNAMICS;
D O I
10.1155/2015/549832
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis.
引用
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页数:13
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