Optimization is the process of finding the best solution among a set of possibilities, by adjusting the parameters and variables in a given problem. It is a central concept in many fields, including mathematics, engineering, and computer science. Optimization problems can take many forms, such as finding the minimum or maximum value of a mathematical application (e.g. function) or finding a solution that satisfies certain constraints. Many different techniques can be used to solve optimization problems, such as gradient descent, Newton’s method, and genetic algorithms. These methods have varying levels of applicability and performance depending on the problem at hand.
In this study, we will look at different ways to find the best solution by adjusting the settings of the problem. The methods that used in this study will be implemented using Python Programmation language. These are: #descent, simulated annealing, guided local search, Tabu search, and genetic algorithms. In addition, we will show a comparison of those methods in terms of the differences, pros, and cons of each method.
- Implementation of each algorithm.
- Application of the algorithms in some problems.
- Schema describes the algorithm.
- charts of each algo...
