Hybrids of Ant Colony Optimization Algorithm- A Versatile Tool

Ant Colony Optimization Algorithm is a meta-heuristic, multi-agent technique that can be applied for solving difficult NP-Hard Combinatorial Optimization Problems like Traveling Salesman Problem (TSP), Job Shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP) and many more. The Positive Feedback Mechanism and Distributed Computing ability makes it very robust in nature. The artificial ants implement a randomized construction heuristic which makes probabilistic decisions as a function of artificial pheromone trails to solve the problems that are dependent on the input data. In spite of ACO having global searching ability and high convergence speed towards optimal solutions, it has some limitations like low population scattering ability and no systematic way of startup. To overcome these problems, various hybrids of ACO with other algorithms like Dynamic Programming, Genetic Algorithm and Particle Swarm Optimization have been proposed to provide better results than using ACO in isolation. This paper studies various approaches for the development of Hybrids of ACO Algorithm for different types of applications and effects thereof. IndexTerms - Ant Colony Optimization Algorithm, Pheromones, Genetic Algorithm, Particle Swarm Optimization