Concepts of Metaheuristics

Ant Colony Optimization or ACO in short is now widely used as an optimization technique for discrete optimization problems. This algorithm mimicks the food foraging behaviour of an Ant Colony. Ant Colony Optimization is a powerful tool in problem-solving due to its ability to find near-optimal solutions in complex, dynamic environments. By leveraging the principles of swarm intelligence, this algorithm can effectively tackle a wide range of optimization problems with multiple variables and constraints. For example, in the field of logistics, Ant Colony Optimization has been successfully applied to vehicle routing problems, where the goal is to find the most efficient routes for multiple vehicles by mimicking how ants communicate and coordinate with each other to efficiently explore and exploit different routes. This can ultimately lead to cost savings and faster delivery times. Ant Colony optimisation is a powerful tool in problem-solving due to its ability to find near-optimal solutio...

Galactic Swarm Optimization (GSO) is a metaheuristic algorithm inspired by the behavior of space swarms. It combines gravitational force and swarm intelligence concepts to solve optimization problems in various fields.  GSO has been proven effective in solving optimization problems involving high-dimensional search spaces, non-linear relationships, and unsupervised classification problems. By utilizing collective intelligence and decentralized decision-making, GSO can efficiently explore the solution space and converge towards the global optimum. This makes it an important tool for addressing real-world problems in fields such as engineering, finance, and logistics.  In this video, we will delve deeper into the principles behind GSO and explore its applications in various domains. Founding and Honorary Editor  @Mrinmoy Founding and Honorary Editor of HydroGeek https://www.mrinmoy.majumdar.info/ X  /  Insta  /  YouTube  /  LinkedIn  /...