Optimization is a very important technique for different fields of research. In most cases, researchers study processes and analyze them in order to find the parameters that will optimize these processes. In fact, some systems are harder to analyze and optimize compared to others.;Continuous nonlinear functions are considered one of the most difficult problems that can be solved using the conventional analytical methods. For this reason, many meta-heuristic optimization methods have been devised and modified to solve these problems. Most of these meta-heuristic optimization methods were inspired by nature or biological evolution theory.;In this dissertation, two new meta-heuristic optimization algorithms are introduced and discussed. These two algorithms can be used alternatively for solving continuous nonlinear optimization problems and combinatorial problems. The first method introduced in this research is named Average Uniform Algorithm (AUA), and is used to solve continuous nonlinear problems.;The AUA algorithm is principally constructed using uniform distribution to generate random solutions, and then averaging the best solutions to come up with one good solution that will give the optimal value for the optimization problem. The Second Algorithm is used to solve combinatorial problems and it is named as Global Neighborhood Algorithm (GNA).;The two algorithms proposed in this work will be based on balancing between local and global search. Thus, at every iteration of these algorithms two types of possible solutions (global and local) will be generated; to allow for both exploration and exploitation of the search space.;Throughout this dissertation, the two algorithms will be discussed and delineated with examples. The algorithms will be also implemented using MATLAB software.;In the final phase of the research, the results of the AUA and the GNA will be discussed and compared with the results of other meta-heuristic optimization methods.
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