It is inspired by the pheromone communication of the blind ants regarding a good path between colony and the food source in an environment, the phenomenon known as stigmergy. The probability of the ant following a certain route is not only a function of pheromone intensity but also a function of distance to that city, the function known as visibility. The objective of the strategy is to exploit historic i.e. pheromone based and heuristic information to construct candidate solutions each in a probabilistic step-wise manner and fold the information learned from constructing solutions into the history. The probability of selecting a component is determined by the heuristic contribution of the component to the overall cost of the solution and the quality of solution and history is updated proportional to the quality of the best known solution.

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

Journal of Swarm Intelligence and Evolutionary Computation provides an International forum for the publication of papers in the following areas. Artificial Intelligence; Robotics; Modelling & Analysis of swarm particle optimization; Swarm Intelligence; Evolutionary programming & Evolutionary Genetics; Genetic Algorithm & Genetic Programming; Ant colony Optimization; Bacterial Forging; Artificial Life & Digital Organisms; Bioinformatics; Evolutionary Computation; Artificial Immune System; Computing; Nano computing; Computational intelligence, etc. Swarm Intelligence journals are at higher echelons that enhance the intelligence and information dissemination on topics closely related to Swarm Intelligence. Computational methods in synthetic biology play a major role in this journal.

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