TY - GEN
T1 - Electromagnetic antenna configuration optimization using fitness adaptive differential evolution
AU - Chowdhury, Aritra
AU - Ghosh, Arnob
AU - Giri, Ritwik
AU - Das, Swagatam
PY - 2010
Y1 - 2010
N2 - In this article a novel numerical technique, called Fitness Adaptive Differential Evolution (FiADE) for optimizing certain pre-defined antenna configuration is represented. Differential Evolution (DE), inspired by the natural phenomenon of theory of evolution of life on earth, employs the similar computational steps as by any other Evolutionary Algorithm (EA). Scale Factor and Crossover Probability are two very important control parameter of DE since the former regulates the step size taken while mutating a population member in DE. This article describes a very competitive yet very simple form of adaptation technique for tuning the scale factor, on the run, without any user intervention. The adaptation strategy is based on the fitness function value of individuals in DE population. The feasibility, efficiency and effectiveness of the proposed algorithm for optimization of antenna problems are examined by a set of well-known antenna configurations.
AB - In this article a novel numerical technique, called Fitness Adaptive Differential Evolution (FiADE) for optimizing certain pre-defined antenna configuration is represented. Differential Evolution (DE), inspired by the natural phenomenon of theory of evolution of life on earth, employs the similar computational steps as by any other Evolutionary Algorithm (EA). Scale Factor and Crossover Probability are two very important control parameter of DE since the former regulates the step size taken while mutating a population member in DE. This article describes a very competitive yet very simple form of adaptation technique for tuning the scale factor, on the run, without any user intervention. The adaptation strategy is based on the fitness function value of individuals in DE population. The feasibility, efficiency and effectiveness of the proposed algorithm for optimization of antenna problems are examined by a set of well-known antenna configurations.
KW - Differential Evolution
KW - Invasive Weed Optimization
KW - Optimization
KW - Particle Swarm Optimization
KW - directivity
UR - http://www.scopus.com/inward/record.url?scp=78650884350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650884350&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17563-3_11
DO - 10.1007/978-3-642-17563-3_11
M3 - Conference contribution
AN - SCOPUS:78650884350
SN - 3642175627
SN - 9783642175626
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 87
EP - 94
BT - Swarm, Evolutionary, and Memetic Computing - First International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2010, Proceedings
T2 - 1st Swarm, Evolutionary and Memetic Computing Conference, SEMCCO 2010
Y2 - 16 December 2010 through 18 December 2010
ER -