Paper Info
Title
Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control
Abstract
A novel multiobjective optimization immune algorithm in dynamic environments, as associated with Pareto optimality and immune metaphors of germinal center in the immune system, is proposed to deal with a class of dynamic multiobjective optimization problems which the dimension of the objective space may change over time. Several immune operators, depending on both somatic maturation and T-cell regulation, are designed to adapt to the changing environment so that the algorithm can achieve a reasonable tradeoff between convergence and diversity of population, among which an environmental recognition rule related to the past environmental information is established to identify an appearing environment. Preliminary experiments show that the proposed algorithm cannot only obtain great superiority over two popular algorithms, but also continually track the time-varying environment. Comparative analysis and practical application illustrate its potential.
Year
DOI
Venue
2008
10.1016/j.asoc.2007.07.005
Appl. Soft Comput.
Keywords
Field
DocType
greenhouse control,pareto optimality,immune system,time-varying environment,multiobjective optimization immune algorithm,dynamic multiobjective optimization problem,dynamic multiobjective optimization,immune metaphor,proposed algorithm,germinal center,immune operator,environmental recognition rule,immune algorithm,dynamic environment,popular algorithm,comparative analysis,multiobjective optimization
Convergence (routing),Population,Mathematical optimization,Algorithm,Greenhouse,Multi-objective optimization,Multiobjective optimization problem,Artificial intelligence,Operator (computer programming),Mathematics,Pareto principle
Journal
Volume
Issue
ISSN
8
2
Applied Soft Computing Journal
Citations 
PageRank 
References 
59
1.59
14
Authors
1
Name
Order
Citations
PageRank
Zhuhong Zhang118616.41