الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Finding the best solution of many feasible solutions that satisfy the objectives and have no conflict with the problem constraints is one of the most important engineering problems which is crucial for every single task and operation. For optimizing problems that have two or more objective functions, it is more different than that for a single objective problem because objective functions mostly have a conflict with each other. For example, improving the power of an engine could be simple by maximizing the fuel but how about if the emission is also required to be minimized? this is a big challenge for decision-makers (DMs) because it is not possible to find one solution that realizes two conflicting purposes. So, the other way of thinking is to determine the set of solutions which are the best for at least one objective and the most feasible for DMs about the other objectives. The classical methods try to solve Multi-Objective Optimization Problems (MOOP) based on converting it to a single objective problem by combining all objectives into one objective function which is not accurate and did not satisfy global optimization. Therefore, this research presents a solution for MOOP based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Reference-based Non-dominated Sorting Genetic Algorithm (RNSGA-II). These methods have been implemented along with conventional Genetic Algorithm (GA) and particle swarm optimization (PSO) for validation and comparing the performance parameters. The coding and simulation have been done using the MATLAB math work, version 2020. Many test problems of the Zitzler-Deb-Thiele test function (ZDT) have been utilized for testing the developed methods. Results showed a significant improvement in the accuracy and convergence speed when using the NSGA-II and RNSGA-II as compared with the results obtained from other methods like GA and PSO.
Keywords: Pareto optimization, NSGA-II, RNSGA-II, Particle Swarm, Genetic algorithm