Paper Info
Title
Constrained Multiobjective Optimization Immune Algorithm: Convergence and Application
Abstract
A new optimization technique, multiobjective optimization immune algorithm for constrained nonlinear multiobjective optimization problems is designed based on immune metaphors of humoral immune and Pareto optimality, especially, some interactive metaphors between antigen population and antibody population. It includes four main mechanisms:(1)constraint-handling operation that provides an alternative feasible solution set for rapidly finding Pareto optimal solutions; (2)antibody evolution associated with clonal selection principle and ideas of immune regulation; competition and update of antigens that induces evolution of antibody populations; (3)memory pool used for collecting the best solutions of evolving antibody populations. Convergence is proven through Markov theory as well as demonstrated by the experiment results. Comparative analysis and applications illustrate that it is effective and valuable.
Year
DOI
Venue
2006
10.1016/j.camwa.2006.10.016
Computers & Mathematics with Applications
Keywords
Field
DocType
pareto optimal solution,new optimization technique,pareto optimality,immune optimization algorithm,convergence,antibody population,antigen population,nonlinear multiobjective optimization problem,multiobjective optimization immune algorithm,immune metaphor,immune regulation,constrained multiobjective optimization,constrained multiobjective optimization immune,antibody evolution,humoral immune,comparative analysis,multiobjective optimization
Convergence (routing),Population,Mathematical optimization,Markov process,Multi-objective optimization,Memory pool,Solution set,Clonal selection,Mathematics,Pareto principle
Journal
Volume
Issue
ISSN
52
5
Computers and Mathematics with Applications
Citations 
PageRank 
References 
4
0.61
7
Authors
1
Name
Order
Citations
PageRank
Zhuhong Zhang118616.41