الفهرس Only 14 pages are availabe for public view
 |  | from | 153 |  |  |
| | | from | 153 | | |
Abstract
Many real-world problems involve two types of problem difficulty: I) multiple, conflicting objectives and II) a highly complex search space. On the one hand, instead of a single optimal solution competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties.
Evolutionary algorithms possess several characteristics that are desirable for this kind of problem and make them preferable to classical optimization methods. In fact, various evolutionary approaches to multiobjective optimization have been proposed since 1985, capable of searching for multiple Pareto optimal solutions concurrently in a single simulation run.
The subject of this work is the improvement of multiobjective evolutionary algorithms and their application to engineering problems. In detail, the major contributions are:
Hybridization of the GAs with Fuzzy logic controller was attempted in chapter 2. We showed through experimental results that the obtained result is always better than classical GA.
A novel approach to multiobjective optimization problems IT-CEMOP, the IT-CEMOP algorithm, is proposed and implemented in chapter 3. It combines both established and new techniques in a unique manner.
New performance metric based on TOPSIS method was proposed and implemented to design speed reducer gear box in chapter 4. The results clearly show that our performance metric gives a quick and good means of assessing progress towards true Pareto optimal solution compared to other metrics.
-The proposed approach was applied to economic emission load dispatch optimization problem formulated as multiobjective optimization problem with competing fuel cost, and emission in chapter 5. Moreover, TOPSIS method is employed to extract the best compromise solution (operating point of the standard IEEE 30-Bus 6-Genrator) from the trade-off curve.
This thesis consists of six main chapters; these chapters can be described in the following manner:
CHAPTER 1 : This chapter focuses on different techniques for solving multiobjective optimization problems, and it is organized as follows: In section 1.1, an introduction to multiobjective optimization problem is introduced. In section 1.2, the general multiobjective optimization problem and its concepts are declared. Section 1.3, describes optimization methods and its classification. In section 1.4, evolutionary algorithms to perform multiobjective optimization are introduced. Section 1.5 outlined TOPSIS method. Section 1.6 describes preliminaries in fuzzy sets theory and their application in fuzzy logic controller.
CHAPTER 2 : This chapter is organized as follows: Sections 2.1, 2.2, 2.3 describe NLP and GAs. In section 2.4, fuzzy logic controllers are introduced. Section 2.5 outlined the combined GA-FLC. An experimental verification of the combined algorithm is carried out in section 2.6.
CHAPTER 3 : This chapter is organized as follows: In section 3.1, 3.2, introduction and Algorithms for Convergence and Diversity for MOO are introduced. In section 3.3, constraint multiobjective optimization via genetic algorithm is introduced, and experimental results are carried out in section 3.4. Finally concluding remarks are followed in section 3.5.
CHAPTER 4 : This chapter is organized as follows: Sections 4.1, 4.2 review some recently performance metric used in MOEA. In section 4.3, new metric is introduced and it was implemented to speed reducer gear box in section 4.4. Performance assessment and comparisons of the proposed algorithm is carried out in section 4.5. Finally concluding remarks are followed in section 4.6.
CHAPTER 5 : In this chapter, we investigated the possibility of the proposed approach to solve multiobjective economic emission load dispatch problem. The optimal power system operation is attained by TOPSIS method, which used to identify solution from a finite set of alternatives.
CHAPTER 6 : This chapter describes some concluding remarks, recommendations and some points for further researches.