Ant colony optimization (ACO) is a technique of optimization that was introduced in the early 1990’s by Dorigo. It is an heuristics optimization for solving combinatorial optimization problems. The inspiring source of ant colony optimization is the foraging behaviour of real ant colonies. This behaviour is exploited in artificial ant colonies for the search of approximate solutions to discrete and continuous optimization problems. It is also applicable to the solution of important problems in telecommunications, such as routing and local balancing. In particular, the agents, called ants, are very efficient at sampling the problem space and quickly finding good solutions to it. Motivated by the advantages of ACO in combinatorial optimization, we considered the maximization of profit in some manufacturing industries in Lagos state of Nigeria, using the ACO method. On comparing our results with the Fibonacci search method, it was established that it compares favourably with it.
Keywords: Ant Colony Optimization, Chemical Pheromone Trails., Combinatorial Optimization, Swarm Intelligent Systems
