Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection

Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated...

Full description

Saved in:
Bibliographic Details
Published in:Evolutionary computation 2008-06, Vol.16 (2), p.225-255
Main Authors: Gong, Maoguo, Jiao, Licheng, Du, Haifeng, Bo, Liefeng
Format: Article
Language:English
Subjects:
Algorithms
artificial immune system
Biological Evolution
Clone Cells
Computational Biology
crowding-distance
evolutionary algorithm
Models, Genetic
Models, Immunological
Models, Statistical
Multiobjective optimization
Mutation
Pareto-optimal solution
Recombination, Genetic
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems, and three low-dimensional problems. The statistical analysis based on three performance metrics including the coverage of two sets, the convergence metric, and the spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.
ISSN:1063-6560
1530-9304
DOI:10.1162/evco.2008.16.2.225