Ant Colony Algorithms in Diverse Combinational Optimization Problems -A Survey
K.Thangavel¹, M.Karnan^2*, P.Jeganathan², A.Petha lakshmi³, R.Sivakumar⁴, G.Geetharamani^5
1Department of Computer science, Periyar University, Salem, India
²Department of computer science, Gandhigram Rural Institute-Deemed University, Gandhigram-624302, TamilNadu, India.
³Department of Computer science, Mother Theresa University, Kodaikkanal, India
⁴Department of R&D, Hindusthan college of Engg and Tech, Coimbatore,
⁵Department of Computer science Bharathidasan University, Trichy,india

Abstract:

Ant Colony Optimization (ACO) metaheuristic is a recent population-based approach inspired by the observation of real ants colony and based
upon their collective foraging behavior. In ACO, solutions of the problem are constructed within a stochastic iterative process, by adding solution
components to partial solutions. Each individual ant constructs a part of the solution using an artificial pheromone, which reflects its experience accumulated while solving the problem, and heuristic information dependent on the problem. In this survey paper, it is intended to summarize the methods of ant colony system used in various types of applications. In particular, routing, assignment, scheduling, subset, machine learning and network routing problems.

Keywords: Combinatorial Optimization, metaheuristics, ant colony system, pheromone.

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