Local Search

Local search algorithms start from an initial solution and iteratively try to improve it through local moves.

• ITERATIVE IMPROVEMENT – HILL CLIMBING
  • A move is only performed if the solution it produces is better than the current solution (also called hill-climbing)
  • The algorithm stops as soon as it finds a local minimum
• META HEURISTICS
• GENETIC ALGORITHM

Swarm Intelligence

• Ant Colony Optimization ACO
  • Ants deposit pheromone trails while walking from the nest to the food and vice versa
  • Ants tend to choose (more likely) the paths marked with higher pheromone concentrations.
  • Cooperative interaction leads to the emergent behavior to find the shortest path
• Artificial Bee Colony Algorithm
  • Artificial bees of three types**:**
    • employed bees that are associated with a specific nectar source,
    • onlookers that observing the employed bees chose a nectar source and
    • scouts that discover new food sources
• Particle Swarm Optimization PSO
  • With a common objective, a single individuals that finds a food source has two alternatives:
    • Move away from the group to reach the food (individualistic choice)
    • Stay in the group (social choice)
  • If more than one individual entity moves toward the food other flock members do the same
  • Gradually the whole group changes direction toward promising areas. The information propagates to all members.