Multi-objective optimization is a research area that focuses on optimizing multiple conflicting objectives simultaneously. In traditional single-objective optimization, the goal is to find the best solution for a single objective function. In contrast, multi-objective optimization deals with the challenge of finding a set of solutions that represent a trade-off between the different objectives. This field is used in various applications such as engineering design, system analysis, and decision-making processes where multiple criteria need to be considered. Multi-objective optimization algorithms aim to find a set of Pareto-optimal solutions, where no solution can be improved in one objective without degrading in another. Some popular algorithms used in multi-objective optimization include NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition).