الفهرس 
CHAPTER 1: Introduction to OptimizationOnly 14 pages are availabe for public view
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Abstract
However, the results obtained by meta-heuristic methods required a considerable amount
of computational time, especially for a large system. Recently, some hybrid methods
combining meta-heuristics with deterministic methods or other meta-heuristics are also
investigated in order to utilize the feature of one method to overcome the drawback of
another method or to benefit from the advantage of both methods. These hybrid methods
are claimed to accommodate the constraints that are more complicated and claimed to
have better quality solutions even though the system under consideration is very large.
In this work, we propose an intelligent computing based approach for solving UC
problems using a binary-real coded genetic algorithm based on K-means clustering
technique. The algorithm integrates the main features of the binary-real coded GA, and kmeans
clustering technique. K-means clustering technique is used in order to divide the
population into a specific number of subpopulations. In this way, different GA operators
can apply to subpopulations instead of one GA operator applied to all population. In
addition, the proposed algorithm solves a fuzzy model for multi-objective unit
commitment problem (MOUCP).
This thesis consists of six main chapters. These chapters can be described in the
following manner:
CHAPTER 1: The most important aim of this chapter is to introduce the basic concepts
and definitions of single and multi-objective optimization. In addition, the
classification of optimization methods is introduced.
CHAPTER 2: This chapter discusses the working principle and the Implementation of
GA. Furthermore, the different ways of encoding; selection, crossover, and mutation
are presented.
CHAPTER 3: A new algorithm is proposed to solve multi-objective resource allocation
problems (MOPAP) through applying one of the evolutionary algorithms, GA, based
on k-means clustering technique. Two test problems are taken from the literature are,
used to compare the performance of the competing algorithms. Moreover an example XIIof
optimum utilization of human resource in the reclamation of derelict land in
Toshka-Egypt is provided.
CHAPTER 4: In this chapter, the formulations of UC problems are presented.
Furthermore, a closer review of some several methods for solving UC problem which
had been reported in the literature is presented.
CHAPTER 5: This chapter investigates the UC problem by binary – real coded genetic
algorithm based on k-mean clustering algorithm which integrates the main features of
a binary-real coded genetic algorithm (GA) and k-means clustering technique. To
evaluate the performance of the proposed algorithm, two test power systems
available in the literature are solved at a different number of clusters as single
objective multi-objective problems and compared with the previous studies.
Furthermore, this chapter presents a fuzzy model for the multi -objective UC
problem. The model takes the uncertainties in fuel cost coefficients in a fuzzy frame.
CHAPTER 6: This chapter describes some concluding remarks, recommendations, and
some points for further researches.