Integration and Comparative Experiments of Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization – Ready-to-Use with Minor Modifications

In this article, we conduct an in-depth exploration of the integration and comparative experiments involving Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). We provide detailed explanations of the underlying theories and concepts of these algorithms, along with their applications across various domains. From an implementation perspective, GA typically involves operations like selection, crossover, and mutation to evolve a population of solutions. SA uses a temperature-controlled probabilistic acceptance of worse solutions to escape local optima. PSO updates particle velocities and positions based on individual and social learning. Through this study, you will gain a comprehensive understanding of how these algorithms work, their respective strengths and limitations, and learn to apply them effectively in your projects. The provided framework can be easily modified with fitness functions and parameters tailored to specific problems. We believe this resource will offer valuable insights and practical support for your learning and professional work.