Paper Info
Title
Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator
Abstract
Numerical optimization of given objective functions is a crucial task in many real-life problems. This paper introduces a new immunological algorithm for continuous global optimization problems, called opt-IMMALG; it is an improved version of a previously proposed clonal selection algorithm, using a real-code representation and a new Inversely Proportional Hypermutation operator.We evaluate and assess the performance of opt-IMMALG and several others algorithms, namely opt-IA, PSO, arPSO, DE, and SEA with respect to their general applicability as numerical optimization algorithms. The experiments have been performed on 23 widely used benchmark problems.The experimental results show that opt-IMMALG is a suitable numerical optimization technique that, in terms of accuracy, outperforms the analyzed algorithms in this comparative study. In addition it is shown that opt-IMMALG is also suitable for solving large-scale problems.
Year
DOI
Venue
2006
10.1145/1141277.1141501
SAC
Keywords
Field
DocType
new immunological algorithm,continuous global optimization problem,comparative study,unconstrained global optimization,numerical optimization algorithm,crucial task,suitable numerical optimization technique,numerical optimization,proportional hypermutation operator,clonal selection algorithm,benchmark problem,new inversely proportional hypermutation,artificial immune systems,artificial immune system,objective function,global optimization
Artificial immune system,Somatic hypermutation,Mathematical optimization,Global optimization,Computer science,Test functions for optimization,Meta-optimization,Operator (computer programming),Optimization algorithm,Clonal selection algorithm
Conference
ISBN
Citations 
PageRank 
1-59593-108-2
29
1.75
References 
Authors
4
3
Name
Order
Citations
PageRank
vincenzo cutello155357.63
Giuseppe Nicosia247946.53
Mario Pavone321219.41