Optimization of Electromagnetic Devices Using Artificial Immune Systems

Lucas de S. Batista, Frederico G. Guimarães, Prakash Paul, Jaime A. Ramírez

Abstract


Optimization algorithms based on principles inspired from the immune system are capable of achieving an arbitrary set of optima, including the global solution. These algorithms differ in the way they implement the encoding, cloning, maturation and replacement steps, which are the basic ingredients of optimization algorithms based on artiï¬cial immune systems. This paper presents the Distributed Clonal Selection Algorithm (DCSA), which employs different probability distributions for the maturation step. The performance of the DCSA is compared with the Real-Coded Clonal Selection Algorithm (RCSA) and the B-Cell Algorithm (BCA) in the design of a waveguide and in the TEAMbenchmark problem 22. The DCSA presents better convergence speed, in terms of number of evaluations, being 8% faster than the RCSA and 78% faster than the BCA, for the minimization of the return loss of a 3D waveguide impedance transformer. In the 8D TEAM problem, the DCSA and RCSA respect the energy constraint with a maximum error of 2.2% while the BCA presents high violations. Regarding these methods, the DCSA achieves better values for the stray magnetic flux density.

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References


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