Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research

In this thesis work, we introduce a big picture of merging two areas; chaos and optimization. We try to address the features of this merge, borders, limitations, and optimal methods of incorporations of chaos to optimization. Three main areas of optimization were studied for chaos combination; evolutionary algorithms (EAs), multi - objective optimization (MOP), and multi - criteria decision making (MCDM). We proposed chaotic topology for swarm intelligent techniques in EAs field, chaotic external archive scheme for the multi - objective evolutionary algorithms (MOEAs) field, and finally polynomial chaos for MCDM field. For the first and second areas; suitable benchmarks, metrics, and test functions are adopted for comparisons and experiments. Several proposed chaotic algorithms are illustrated in both single and multi - objective versions. For MCDM, five real-life categories of applications are illustrated and presented

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