| In the nature ants and bees often can achieve a complex task while a single bodymay be failure. Swarm Intelligence emerged out of social insect collective behavior.Swarm Intelligence is defined as any attempt to design algorithms or distributedproblem-solving devices inspired by the collective behavior of the social insertcolonies and other animal societies. Swarm Intelligence exhibits a number ofinteresting properties such as flexibility, robustness, decentralization andself-organization. The instances of these algorithms on the domains of optimization,telecommunication network, knowledge discovery and robots are obviously increased.Swarm Intelligence has become a hot research area for Artificial Intelligenceresearchers.Ant Colony Optimization Algorithms is a kind of evolution computationalgorithms base on bionics, which has the character of positive feedback and parallelprocessing. Ants can find the best route between a hole and foods. The reason is thatthe ant release volatile pheromone in the trail. Ants communicate with others by thepheromone that is left on the road and their behaviors are trending to the trail of densepheromone. Because the pheromones in the optimal route volatile less, there are moreand more ants pass through this routes and strengthen the optimal route. The processof the optimal route is forming by positive feedback. Ant colony algorithm was firstput forward to solve the famous traveling Salesman problem by Marco Dorigo and hiscolleagues. Now it has been successfully used to solve many kinds of combinationoptimization problems such as quadratic assignment, graph coloring, vehicle route,sequential ordering and so on.This dissertation focuses on a number of Swarm Intelligence models. The goal ofthis dissertation is to explore and further proof the properties of the algorithms. On theother hand, this thesis extends the application domains of swarm intelligence. Thecontributions of this dissertation are as follows: (1) Research on ant colony algorithms. A subsection exchange ant colonyalgorithm of solving TSP is presented. The idea of small windows, random subsectionand enough exchange of Simulated Annealing are introduced into ant colonyalgorithm. It combines ant colony algorithm with Simulated Annealing. On thecondition of falling in local best of ant colony algorithm, it can improve the precision.It also can reduce the number of searching. Simulation results show that better effectsare obtained.(2) Research on appliocation of ant colony algorithm to 0-1 knapsack problem.0-1 knapsack problem is a difficult NP problem. Ant colony algorithm was appliedsuccessfully to many hard combinational optimization problems. So, An ant colonyalgorithm of solving 0-1 knapsack problem is introduced.By optimizing it, a quickant colony algorithm of solving 0-1 knapsack problem is presented. When the numberof article is big it also can obtain better effects.(3) Research on appliocation of ant colony algorithm to circle permutationproblem. A quick ant colony algorithm of solving circle permutation problem is putforward. By optimizing it, it greatly reduces the searching time of ant colonyalgorithm. It also effectively ameliorates the disadvantage of easily falling in localbest of ant colony algorithm.(4) Research on an improved ant colony algorithm and two types of applicationmodels. By changing the time of calculating the probability and using of roulette-wheel which sum of probability is u and subsection exchange based on SimulatedAnnealing, an improved ant colony algorithm is put forward. Two types of applicationmodels of ant colony algorithm are also presented.(5) Research on solving N queens problem based on swarm intelligence. An antmodel algorithm of solving N queens problem is put forward. It is inspired by swarmintelligence and ant algorithm and multi-Agent system. It absorbs advantages of backtrace algorithm. Ant model algorithm is a random searching algorithm. It radicallychanges the technique of systemic searching and avoids large amount of redundantsearching and ensures the necessary searching. It greatly reduces searching time and... |
PDF Full Text Request