Solving Traveling Salesman Problem by Using Improved Ant Colony Optimization Algorithm

Abstract

Ant colony optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization problems and is taken as one of the high performance computing methods for Traveling salesman problem (TSP). TSP is one of the most famous combinatorial optimization (CO) problems and which has wide application background.. ACO has very good search capability for optimization problems, but it still remains a computational bottleneck that the ACO algorithm costs too much time to convergence and traps in local optima in order to find an optimal solution for TSP problems. The presented paper proposes an improved ant colony optimization algorithm with two highlights. First, candidate set strategy is adopted to rapid convergence speed. Second, a dynamic updating rule for heuristic parameter based on entropy to improve the performance in solving TSP. Algorithms are tested on benchmark problems from TSPLIB and test results are presented. From our experiments, the proposed algorithm has better performance than the conventional ACO algorithm and the results of the proposed algorithms are found to be satisfactory.

An Enhanced Ant Colony-based Approach for Query Optimization

“…In fact, an ACO algorithm uses a set of artificial ants (individuals) which transact to reach the solution of a given problem by exchanging information via pheromone deposited on graph edges [17]. The ACO algorithm is employed to imitate the behavior of real ants and it can be represented as depicted in Algorithm 1 [8]. The proposed approach to estimate the optimal QAP by ACO has the following steps:…”

“…Compute the new pheromone value of the inter node representing the result of joiningrelationsi and j using equation (8).…”

Hybrid Algorithm Based on Ant and Genetic Algorithms for Task Allocation on a Network of Homogeneous Processors

“…The idea of Ant Colony Algorithms is suggested based on the behavior of real ants , real ants are a clever creatures that are capable to find the shortest path to the source of food from the nest without using visual cues ,And if the path to the food source is damaged by any ostacle ,then the ants are also capable of adapting to these changes in the environment,so that they also find a new shortest path [4][5] [7]. During their walk ants put a small amount of natural material called pheromone on the ground this tell other ants that it follow this path if an ant reaches to the source food in a shorter path then it will return to the nest before the other, and its path will have more pheromone than the other pathes followed by the other ants, and this give a mark to the other ants that this path is followed by greater number of ants so that the other ants will follow this path because ants prefer to follow a path rich in pheromone and they will put another pheromone on the path these steps will continue until all or most of the ants follow the same path which is the shortest.…”

Pheromone-based Dynamic Coordination for Manufacturing System

“…Thus an appropriate approach is necessary to coordinate and integrate the behavior of manufacturing system. Considering the already developed ant colony optimization (ACO) methodology [7], ACO has a powerful capacity to find out good solutions to combinatorial optimization problems, Therefore, ACO has been widely applied to solving various combinatorial optimization problems such as traveling salesman problem (TSP) [8], job-shop scheduling problem (JSP) [9], task allocation problem [10], etc. This paper uses an indirect coordination approach based on pheromone to optimize global performance for manufacturing system.…”