Final Artificial Intelligence 111521 Project offered by Dr. Shamaee
You can find the whole concept of the project in this Link
Genetic algorithms (GAs) are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome-like data structure and apply recombination and mutation operators to these structures so as to preserve critical information. GAs are often used to solve optimization and search problems, relying on bio-inspired operators such as selection, crossover, and mutation. By mimicking the process of natural selection, genetic algorithms can evolve solutions to complex problems through a survival-of-the-fittest approach.
NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a popular multi-objective optimization algorithm. It is an evolutionary algorithm that uses a form of genetic representation and fitness-based selection to guide the search for optimal solutions. It is particularly well-suited for problems where the goal is to optimize multiple conflicting objectives.
MOPSO is a multi-objective optimization algorithm based on particle swarm optimization (PSO). It is designed to find the Pareto-optimal set of solutions in a multi-objective optimization problem. MOPSO is a population-based algorithm that maintains a set of candidate solutions and iteratively improves them through the application of PSO operators such as velocity update and position update. It is particularly well-suited for problems where the goal is to optimize multiple conflicting objectives.