Electromagnetic Antenna Configuration Optimization Using Fitness Adaptive Differential Evolution

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.

[1]  J. Ackermann,et al.  Analysis and Design , 1993 .

[2]  C. Balanis.  Antenna theory , 1982 .

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[5]  Constantine A. Balanis,et al.  Antenna Theory: Analysis and Design , 1982 .

[6]  M. F. Pantoja,et al.  Benchmark Antenna Problems for Evolutionary Optimization Algorithms , 2007, IEEE Transactions on Antennas and Propagation.

[7]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[9]  E.J. Rothwell,et al.  Investigation of Simulated annealing, ant-colony optimization, and genetic algorithms for self-structuring antennas , 2004, IEEE Transactions on Antennas and Propagation.

[10]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[11]  Yahya Rahmat-Samii,et al.  Electromagnetic Optimization by Genetic Algorithms , 1999 .

[12]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[15]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.