Abstract
Optimisation, the process of finding either a maximum of a minimum of the problem at hand plays
a key role in several disciplines including engineering and science. In this thesis, different Cuckoo
Search algorithms are developed for effective optimisation purposes. These algorithms are tested
on ten mathematical test functions and then used to optimise a Back-Propagation Neural Network
used for short-term electricity load forecasting for South African data, with the focus on the City
of Johannesburg. The original Cuckoo Search algorithm is based on random walk step sizes
derived from Lévy probability distribution and the switching parameter between local and global
random walks is constant. However, other probability distributions like Cauchy, Gaussian and
Gamma have also been used and the switching parameter can be changed dynamically.
The first contribution of the thesis is the development a new Cuckoo Search algorithm whose
random step sizes are derived from Pareto probability distribution function. This new Pareto-based
Cuckoo Search algorithm is tested on ten benchmark test functions together with other Cuckoo
Search algorithms using step sizes derived from Gaussian, Cauchy, Gamma and Lévy probability
density functions. When using the confidence interval analysis, the Lévy-based Cuckoo Search
algorithm outperforms the Pareto based Cuckoo. However, confidence interval results are only
superior due to only one test function whereby Lévy-based Cuckoo Search performed well.
Moreover, the Pareto-based Cuckoo shows superior performance in comparison to the other
algorithms, leading in seven test functions out of ten when tested for convergence.
The second contribution is the implementation of Cuckoo Search algorithms with dynamically
increasing switching parameters between local and random walks. The first improvement done on
Cuckoo Search algorithm is the implementation of linear increasing switching parameter, the
second is the implementation of power increasing switching parameter and the third improvement
is the implementation of exponential increasing switching parameter. When tested on benchmark
test functions, the exponentially increasing Cuckoo Search algorithm outperforms the other
algorithms by obtaining the longest confidence interval of 4.50566 while the next algorithm
(original Cuckoo Search) obtains an interval of 3.9699. Moreover, using convergence plots, both...
D.Ing. (Electrical and Electronic Engineering)