Question

Most researchers claim Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) approaches are widely used for the optimization of the algorithms. Explain such optimization algorithms using real life examples.

Answer 1

1). ANSWER :

GIVENTHAT :

Most researchers claim Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) approaches are widely used for the optimization of the algorithms. Explain such optimization algorithms using real life examples.

Particle Swarm Optimization (PSO) :-

Understand using scenario: suppose many birds are randomly searching food in an area. but There is only one piece of food in the area being searched. but birds do not know where the food is. But they know how far the food is in each iteration. So what's the best strategy they used to find the food? The effective one is to follow the bird which is nearest to the food.

as scenario In PSO, every single solution is a "bird" in the search space. We call it "particle". All particles have fitness values that are evaluated by the fitness function to be optimized and have velocities which direct the flying of the particles.

Ant colony optimization (ACO):-

This algorithm is introduced based on the foraging behavior of an ant for seeking a path between their colony and source food. Ant lives in colonies. The behavior of the ants is controlled by the goal of searching for food. While searching, ants roaming around their colonies. An ant repeatedly hops from one place to another to find the food. While moving, it deposits an organic compound called pheromone on the ground. Ants communicate with each other via pheromone trails. When an ant finds some amount of food it carries as much as it can carry. When returning it deposits pheromone on the paths based on the quantity and quality of the food. Ant can smell pheromone. So, other ants can smell that and follow that path. The higher the pheromone level has a higher probability of choosing that path and the more ants follow the path, the amount of pheromone will also increase on that path.