  Artificial Intelligence (AI) techniques are known for its ability in tackling
problems found to be unyielding to traditional mathematical methods. A recent
addition to these techniques are the Computational Intelligence (CI) techniques
which, in most cases, are nature or biologically inspired techniques. Different
CI techniques found their way to many control engineering applications,
including system identification, and the results obtained by many researchers
were encouraging. However, most control engineers and researchers used the
basic CI models as is or slightly modified them to match their needs.
Henceforth, the merits of one model over the other was not clear, and full
potential of these models was not exploited.
  In this research, Genetic Algorithm (GA) and Particle Swarm Optimization
(PSO) methods, which are different CI techniques, are modified to best suit the
multimodal problem of system identification. In the first case of GA, an
extension to the basic algorithm, which is inspired from nature as well, was
deployed by introducing redundant genetic material. This extension, which come
in handy in living organisms, did not result in significant performance
improvement to the basic algorithm. In the second case, the Clubs-based PSO
(C-PSO) dynamic neighborhood structure was introduced to replace the basic
static structure used in canonical PSO algorithms. This modification of the
neighborhood structure resulted in significant performance of the algorithm
regarding convergence speed, and equipped it with a tool to handle multimodal
problems.
  To understand the suitability of different GA and PSO techniques in the
problem of system identification, they were used in an induction motor's
parameter identification problem. The results enforced previous conclusions and
showed the superiority of PSO in general over the GA in such a multimodal
problem.
