Abstract
<jats:p>The best solution to a problem is found via optimization algorithms, which are mathematical processes that frequently involve determining a function's lowest or maximum value. These methods are essential for increasing accuracy and efficiency in a variety of domains. Finding the best answers for a particular optimization problem may depend greatly on the optimization algorithm selection. There are many different types of optimization algorithms. The goal is usually to reduce amount of loss that occurs throughout learning process. Data samples and their corresponding results are fed into a model. Upon producing an output, a model compares it to the intended output, calculates difference between the two, and then tries to make generated output more similar to intended result. An optimization algorithm iterates over multiple cycles until it converges, increasing model's accuracy. This chapter highlights the significance of optimization algorithms in fostering creativity and efficiency in problem-solving by giving a high-level overview of their concepts, classifications, and applications.</jats:p>