Theory And Applications Of Ant Colony Algorithm And Hybridizing Other Algorithms

A population-based simulated evolutionary algorithm called ant colony optimization (ACO for short) was proposed in 1992 by Italian researchers Dorigo M., Maniezzo V. and Colorni A. Many scholars are attracted to study ACO and in the past ten years the algorithm has been widely applied to the fields of combinatorial optimization, network routing, functional optimization, data mining, and path planning of robot etc, and good effects of application are gained.The dissertation focuses on the principles, theory, and applications of ACO, especially, an in-deep and systemic study on how to improve the basic ACO algorithm, solving nonlinear integer programming, continuous optimization problem, clustering problem, hybridizing other algorithms and convergence. The main achievements of this dissertation include:1. A new ACO algorithm for unconstrained nonlinear integer optimization problem is present. Ants move around the set of integers space, and while walking the ants lays down pheromone on the ground. The pheromone is used to direct the search process. Experimental analyses are carried out on the reasonable selection on the parameters of this algorithm, and basic principles for the parameter selection are provided. Results of numerical tests show the effectiveness and generality of the method. By use of the properties of weapon-target assignment problem, multiprocessor scheduling problem and reliability optimization problem, incomplete same methods are presented to solve them. Comparing with other methods, and their effectiveness are illustrated through result. An ant colony algorithm based on multiplicate pheromone is proposed to solve the traveling salesman problems. By use of the properties of pheromone of ant colony algorithm, three modes of updating the pheromone are hybridized. The method uses not only local information but also global information and combines the local search with the global search to improve its convergence. The simulation results for TSP show the validity of this algorithm. An ACO algorithm for solving continuous optimization problem is presented. By dividing the space of solution into many grids, ants first find the grid in which the trail information is most. Ants squeeze the intervals and repeat the process until the interval is as small as desired.2. The clustering problem is a typical problem in pattern recognition. Two kinds of clustering methods based on ant colony algorithm are proposed. One method is to act how ants look for food. While walking the ants lays down pheromone on the ground, which is...