الفهرس 
Chapter 1 Multi-objective Optimization ProblemOnly 14 pages are availabe for public view
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Abstract
In this work a new algorithm for tackling nonlinear constrained multi-objective optimiza-tion problems is introduced. This algorithm, which we call relaxed interactive sequential hybrid optimization technique (RI-SHOT), is based on a hybrid technique between I-SHOT method and step method (STEM) to transform multi-objective problems to single objective ones. An active-set strategy is used together with a penalty method to transform single- objective constrained optimization problem to unconstrained one. A trust-region global-ization strategy is added to the algorithm to solve the obtained unconstrained problem to ensure global convergence. this thesis is organized as follows: In Chapter 1, we introduce the basic denitions which are needed in this thesis and we present some methods which are used to convert the multi-objective optimization problem to single-objective constrained optimization problem. In chapter 2, we introduce basic concepts regarding single-objective optimization problems. Atrst, the formulation and classication of single-objective optimization problems are in- troduced. For each class we present the optimality conditions and one of the basic numerical methods used to solve this class of optimization problems. In Chapter 3, we describe the trust-region strategy for unconstrained optimization problem and show the outline of our active-set penalty trust-region algorithm which is introduced in this thesis to solve the gen-eral single-objective optimization problem. Then we introduce the hybrid algorithm which is used in this thesis to convert multi-objective optimization problem to single-objective. Finally, the main algorithm is described in a owchart. In Chapter 4, we concerned with solving a real engineering problem, the economic emission load dispatch problem (EELD) using the proposed algorithm. Firstly, we implement the proposed RIAPT algorithm to solve the EELD problem. Finally, the results is visualized and compared with other algorithms used to solve the same problem to ensure the e ectiveness of our proposed algorithm. Then, we introduce the conclusion which contains a summery of the previous chapters and the suggested future work in Chapter 5.