Electromagnetic antenna configuration optimization using fitness adaptive differential evolution

Aritra Chowdhury, Arnob Ghosh, Ritwik Giri, Swagatam Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationSwarm, Evolutionary, and Memetic Computing - First International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2010, Proceedings
Number of pages8
StatePublished - 2010
Externally publishedYes
Event1st Swarm, Evolutionary and Memetic Computing Conference, SEMCCO 2010 - Chennai, India
Duration: Dec 16 2010Dec 18 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6466 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st Swarm, Evolutionary and Memetic Computing Conference, SEMCCO 2010

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Differential Evolution
  • Invasive Weed Optimization
  • Optimization
  • Particle Swarm Optimization
  • directivity


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