Date of Award:
Master of Science (MS)
Electrical and Computer Engineering
Edmund A. Spencer
Edmund A. Spencer
In the real world, we encounter a number of problems which require iterative methods rather than heuristic approaches to solve them. Not every problem can be solved with a definitive method. Optimization algorithms come to the aid of such instances. These algorithms carry out multiple iterations or generations to try to achieve the lowest value of a cost function. The demand for fast and robust stochastic algorithms to cater to the optimization needs is very high. The faster the convergence to a low value of the cost function, the better the algorithm is. This is attained in greedy criterion approaches, where the new parameter vector is accepted only if it reduces the value of the cost function. But this may also lead in misconvergence where it could lead the vectors to get trapped in local minima. Parallel search techniques have more exploratory power. So, depending on the application, different suitable techniques are used.
This thesis mainly concentrates on benchmarking three popular algorithms: Real valued Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential
Evolution (DE). The DE algorithm is found to out-perform the other algorithms in fast convergence and in attaining low-cost function values. The DE algorithm is selected and used to build a model for forecasting auroral oval boundaries during a solar storm event. This is compared against an established model by Feldstein and Starkov. As an extended study, the ability of the DE is further put into test in another example of a nonlinear system study, by using it to study and design phase-locked loop circuits. In particular, the algorithm is used to obtain circuit parameters when frequency steps are applied at the input at particular instances.
Raj, Ashish, "Evolutionary Optimization Algorithms for Nonlinear Systems" (2013). All Graduate Theses and Dissertations. 1520.
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