SUMMARY Multi-objective optimization (MO) is a popular field that focuses on addressing multiple, often conflicting objectives to help with decision makings by decision-makers (DM). Rather than a single solution as in single-objective optimization problems, MO seeks for finding a set of solutions known as non-dominated or Pareto optimal solutions. Such solutions represent the best trade-offs among the objectives and provide DMs with a wide range of options to make decisions. Due to the significant importance of the MO, many algorithms have been proposed and divided into two classes: a priori or a posteriori. In the former class, the multi-objective problem is dealt with as a single objective problem by assigning a weight to each objective and using non-linear optimization methods such as Nelder-Mead simplex to solve it. Due to adjusting the weight of each objective by the DM and the difficulty in determining the significance of each one when solving, this approach might not be practical. In the latter class, the posteriori approach has been suggested for overcoming the disadvantages found in the previous method by generating a set containing the whole non-dominated solutions, or a number of the well-distributed non-dominated solutions, and giving those solutions to the decision-makers to choose the adequate solution to its need. Due to spreading the problems with more than one objective in a lot of the fields such as bioinformatics, mechanical engineering, civil engineering, and other fields, the area of MO attracted the researchers over the last decades to propose algorithms with high-abilities on solving this type of problems. As an attempt to know to which point the other researchers overcome this problem and the methodology they follow for getting to the solutions, we will review some of them in the next chapter. This thesis consists of five chapters: Chapter One: This chapter describes the importance of the research topic, highlights the motivations and objectives of this study, and summarizes the contents and organization of the thesis. Chapter Two: Herein, the recently-published papers are extensively reviewed to highlight their contributions to tackling this problem. Chapter Three: This chapter discusses the main steps done for the proposed algorithm, which are represented in summarizing the standard whale optimization algorithm, introducing the techniques integrated with our proposed model, and finally presenting the proposed algorithm and its implementation in addition to its time complexity. Chapter Four: This chapter discusses the findings of the proposed algorithms on some unconstrained benchmarks with convex, non-convex, and disjoint multi-objective problems, in addition to comparing those findings with those of several well-established multi-objective algorithms. Chapter Five: This chapter summarized the research findings in addition to future suggestions for improvement. ABSTRACT Recently, several meta-heuristic and evolutionary algorithms have been proposed for tackling the multi-objective optimization algorithms. Even now, the meta-heuristic and evolutionary algorithms still suffer from some difficulties when solving the multiobjective optimization problems; those difficulties are: (1) accelerating the convergence toward the true Pareto optimal solution, and (2) finding better-distributed solutions on the curve of the true-Pareto front until enabling decision-makers from finding the solutions that may be relevant to their problems. Therefore, in this thesis, the Whale Optimization Algorithm (WOA) is improved and extended to solve the multi-objective optimization problems to alleviate those difficulties. The improvements include: (1) modifying the distance control factor of the standard WOA to contain values generated dynamically instead of a fixed one, (2) the trade-off between moving toward the opposite of the best solution and its original values based on a certain probability to prevent stuck into local minima, and (3) accelerating the convergence and finding better-distributed solutions on the curve of the true-Pareto front using Nelder-Mead method and the Pareto Archived Evolution Strategy (PAES) together in an effective manner. Afterward, the proposed algorithm is tested on three benchmark multi-objective test functions (DTLZ, CEC 2009, and GLT), including 25 test functions, to verify its effectiveness by comparing with nine robust multi-objective algorithms. The experiments demonstrate the superiority of the proposed algorithm compared to some of the existing multi-objective algorithms.