Meta-Heuristic Algorithms

Optimization problems are problems that involve finding the best solution from a set of possible solutions, according to some criteria or objective function. Optimization problems are ubiquitous in computer science, such as scheduling, routing, clustering, machine learning, and more.

However, many optimization problems are very hard to solve exactly, either because they are too complex, too large, or too dynamic. In such cases, finding the optimal solution may require too much time or computational resources. Therefore, we need to find approximate solutions that are good enough for our purposes.

Meta-heuristic algorithms are a type of algorithm that can help us find approximate solutions to optimization problems. They are general-purpose methods that can be applied to different types of problems, without requiring much prior knowledge or problem-specific information. Meta-heuristic algorithms work by iteratively improving a solution until it meets some quality criteria or termination condition.

Meta-heuristic algorithms are inspired by various sources, such as nature, physics, biology, and human behavior. Some examples of meta-heuristic algorithms are:

• Genetic algorithm (GA): Inspired by the process of natural evolution, GA works by maintaining a population of candidate solutions that undergo selection, crossover, and mutation operators to generate new solutions.
• Simulated annealing (SA): Inspired by the process of annealing in metallurgy, SA works by gradually decreasing the temperature of a system that allows random changes in the solution. As the temperature decreases, the changes become less frequent and more likely to improve the solution.
• Tabu search (TS): Inspired by the concept of tabu or forbidden in human culture, TS works by keeping track of a list of recently visited solutions that are prohibited from being revisited. This helps to avoid getting stuck in local optima and explore new regions of the search space.

Here, we will share some of our works in the area of meta-heuristic algorithms and their applications. We will show how meta-heuristic algorithms can help us solve various optimization problems in computer science. We hope you will find these works interesting and useful.