الفهرس 
Genetic Algorithms Applied to OptimizationOnly 14 pages are availabe for public view
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Abstract
Many real-world problems involve two types of problem difficulty : 1) multiple, conflicting objectives and 2) 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. Thus in recent years, intelligent optimization techniques have been a growing interest for solving such complex problems. They model some natural phenomena, based on the principle of evolution (survival of the fittest) they are genetic algorithms, tabu search, simulated annealing, and any other hybrid techniques. They are able to handle problems, which have special features such as discontinuities, multimodality, and disjoint feasible spaces. One of the most important problems we face today is sequential or multistage decision making. In such problems, however, it would be appropriate to consider that possible values of the parameters in the description of the models that usually involve the ambiguity of the experts’ understanding of the real system. from this point of view in this thesis, we focus on how intelligent optimization techniques (e.g., genetic algorithms ) solve multistage decision making problems with fuzzy parameters, and investigate the possibility of using GAs to solve multiobjective routing problem and multiobjective resource allocation problem which are solved previously using dynamic programming approach.
This thesis consists of six main chapters and appendix, 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 it’s concepts. Section 1.3, describes general optimization methods and it’s classification. In section 1.4 different approaches to perform multiobjective optimization are introduced. Section 1.5 describes preliminaries in fuzzy sets theory. In section 1.6 we introduce multiobjective dynamic optimization problems.
CHAPTER 2 : This chapter is organized as follows: sections 2.1, 2.2, 2.3 describe genetic algorithms, definitions, concepts and mechanism. In section 2.4 schema theorem and building block hypothesis. Section 2.5 evolutionary algorithms for multiobjective optimization (five of the most salient MOEAs have been chosen for the comparative studies)
CHAPTER 3 : This chapter is organized as follows: Sections 3.1, 3.2, 3.3 review some recently constraint handling techniques used in nonlinear programming. In section 3.4, modified co-evolutionary genetic algorithm (M-COGA) is introduced and an experimental verification of the proposed algorithm is carried out in section 3.5. Finally concluding remarks are followed in section 3.6.
CHAPTER 4 : This chapter is organized as follows: section 4.1, 4.2, introduction and formulation of Multiobjective Dynamic Programming Problems with Fuzzy Parameters are introduced. In Section 4.3, fuzzy decision model (FDM) is introduced. In section 4.4, we introduce the primal and dual form of the NLPP. In section 4.5, hybrid genetic-dynamic programming algorithm ( HGDPA ) are introduced, and a numerical example on the proposed algorithm is carried out in section 4.6. Finally concluding remarks are followed in section 4.7.
CHAPTER 5 : In this chapter, we investigated the possibility of using genetic algorithms to solve multiobjective routing problems (MORP) and multiobjective resource allocation problem (MORAP). This chapter is organized as follows, the formulation of the multiobjective routing problems (MORP) are described in section 5.2. Section 5.3 gives out multiobjective routing genetic algorithm. In section 5.4, we introduce multiobjective resource allocation problem. Section 5.5 describe MORAP via genetic algorithm. The experimental verification are discussed in section 5.6. Conclusion follows in section 5.7.
CHAPTER 6 : This chapter describes some concluding remarks, recommendations and some points for further researches.
Appendix: Contain six- bus sample power system data.