User:Anandiitmandi/sandbox

=Metahuristics and Ant Colony Optimization=

Metaheuristics
Metaheuristics are solution methods that engineer an interaction between local improvement procedures and higher level strategies to create a process that is capable of not getting trapped into local optima and performing a robust search of a solution space.

These methods also includes any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize neighborhood as a means of considering admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. The degree to which neighborhoods are exploited varies according to the type of procedure.

Numerous strategies emerged from the creation of metaheuristic methods and have proved to be remarkably effective, and become preferred line of attack for solving many types of complex optimization problems.

Metaheuristics vs Exact Methods For Optimization
While metaheuristics do not guarantee the exact optimal solutions, exact procedures (which theoretically provide such a guarantee, if allowed to run long enough) have often proved incapable of finding solutions whose quality is close to that obtained by the leading metaheuristics-particularly for real-world problems, which often attain notably high levels of complexity. In addition, some of the more successful applications of exact methods integrates metaheuristic strategies within them. These outcomes have motivated additional research and application of new and improved metaheuristic methodologies.



=Ant Colony as Metaheuristics= Ant Colony Optimization (ACO) is a metaheuristic approach for solving hard combinatorial optimization problems. The inspiring source of ACO is the pheromone trail laying and following behavior of real ants which use pheromones as a communication medium.

ACO algorithms were first proposed in 1990’s by M. Dorigo and colleagues to solve combinatorial optimization problems and is applied successfully to solve most of the hard combinatorial optimization problems in reasonable time.

Important point of consideration is how the Foraging behavior of ant can be simulated as algorithm that can be run on computer to solve optimization problem and get benefited from the methodology available in nature.

Very first step in solving optimization problems using ACO is to model optimization problem as a graph. Then following are the major steps that are involved in ACO algorithms are
 * 1. Initialization by establish nest on various selected nodes of the graph. Nodes are generally selected randomly and may be more than one.
 * 2. Start hunting for food on various edges of the graph. Edges to start hunting are also selected by ants. After spotting food source each ant deposits a substance, known as pheromone, with the quality and quantity depends on the quality of the food source found on the edges on return. Quantity of the pheromone on the edge effects the path selection decision of subsequent ants.
 * 3. Update pheromone by each ant on their chosen path. Updation can take place locally or can take place globally.
 * 4. Update solution when better solution found.

ACO Variants
The first example of such an algorithm is Ant System (AS), which was proposed using as example application the Traveling Salesman Problem (TSP). Then it is used successfully to solve considerable number of applications including the quadratic assignment, vehicle routing, sequential ordering, scheduling, routing in Internet-like networks, and so on.

Various variants of ACO algorithms are also reported for solving combinatorial optimization problems and then modified and used to solve other classes of problems like continuous-variable optimization problems       mixed-variable optimization problems  and multi-objective optimization problems and has been proved promising.

For more information on ongoing projects, research works and publications visit the webpage.

=References=