الفهرس 
1.2. General Multiobjective Optimization Problem.Only 14 pages are availabe for public view
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Abstract
Over the past few decades there have been a growing interest in the use of biology as a source of inspiration for solving computational problems. This area of research is often referred to as Biologically Inspired Computing. The motivation of this field is primarily to extract useful metaphors from natural biological systems, in order to create effective computational solutions to complex problems in a wide range of domain areas. Many real optimization problems require optimizing multiple conflicting objectives with each other. These problems with more than one objective are called Multiobjective Optimization Problems (MOPs). There
is no single optimal solution , but a set of alternative solutions, these solutions are optimal in the wider sense that no other solutions in the search space are dominate them when all objectives are considered. They are known as Pareto optimal solutions. MOPs naturally arise in many area of knowledge such as economics, machine learning and electrical power system. Evolutionary algorithms (EAs) for MOPs optimize all objectives simultaneously and generating a set of alternative solutions. The simultaneous optimization can fit with population based approaches, such as Genetic Algorithms (GAs). Because they generate multiple solutions in a single run, these population-based approaches are more successful when solving MOPs. In this dissertation we present a new optimization algorithm, the proposed algorithm operates in two phases: in the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm based on concept of coevolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems. The proposed approach is applied to economic environmental dispatch EED) optimization problemwhich formulated as multiobjective optimization problem with competing fuel cost, and emission. Moreover, TOPSIS (Technique for order Preference by Similarity to Ideal Solution) method is employed to extract the best compromise solution (operating point) from the trade-off curve. Dissertation Organization The dissertation is organized in five chapters : Chapter (1) A survey on Related Topics. This chapter is organized as follows, Section 1.1 Introduction, Section 1.2 General Multi- Objective Optimization Problem, Section 1.3 Classification of Some Methods to Perform Multiobjective Optimization , Section 1.4 Evolutionary Algorithms (EAs), Section 1.5 Evolutionary Algorithms for Multiobjective Optimization, Section 1.6 Rough Sets. Chapter(2) Genetic Algorithms for Optimization Problems. This chapter contain the following, Section 2.1 Introduction, Section 2.2 Basic Definitions and Concept, Section 2.3 The Mechanism of Genetic Algorithms, Section 2.4 A simple Example of Applying Genetic Algorithms, Section 2.5 Constrained Optimization using GAs, Section 2.6 Overview of Evolutionary Algorithms for Multiobjective Optimization. Chapter(3) Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm. This chapter is constructed as follows, Section 3.1 Introduction, Section 3.2 Basic Concepts and Definitions, Section 3.3 Constraint Multiobjective Optimization via Genetic Algorithm, Section 3.4 Experimental Results, Section 3.5 Conclusions. Chapter (4) Rough Sets Based Evolutionary Approach for Economic Environmental Dispatch of Power Systems. This chapter contain the following, Section 4.1 Introduction, Section 4.2 Economic Environmental Dispatch (EED) Optimization, Section 4.3 Implementation of the proposed approach, Section 4.4 Results
and Discussions , Section 4.5 Identifying a satisfactory solution, Section 4.6 Conclusions. Chapter(5) Conclusions and Recommendations for Future Researches. This chapter describes some concluding remarks, recommendations and some points for further researches and Recommendations for Future Work.