A genetic algorithm for shortest path routing problem and the sizing of populations

Abstract

Abstract-This paper presents a genetic algorithmic approach to the shortest path (SP) routing problem. Variable-length chromosomes (strings) and their genes (parameters) have been used for encoding the problem. The crossover operation exchanges partial chromosomes (partial routes) at positionally independent crossing sites and the mutation operation maintains the genetic diversity of the population. The proposed algorithm can cure all the infeasible chromosomes with a simple repair function. Crossover and mutation together provide a search capability that results in improved quality of solution and enhanced rate of convergence. This paper also develops a population-sizing equation that facilitates a solution with desired quality. It is based on the gambler's ruin model; the equation has been further enhanced and generalized, however. The equation relates the size of the population, the quality of solution, the cardinality of the alphabet, and other parameters of the proposed algorithm. Computer simulations show that the proposed algorithm exhibits a much better quality of solution (route optimality) and a much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network sizes and topologies) for almost all source-destination pairs. Furthermore, simulation studies emphasize the usefulness of the population-sizing equation. The equation scales to larger networks. It is felt that it can be used for determining an adequate population size (for a desired quality of solution) in the SP routing problem.Index Terms-Gambler's ruin model, genetic algorithms, population size, shortest path routing problem.

Routing Algorithm using mobile agents and Genetic Algorithm

“…GA provides alternative methods for solving problems which are difficult to solve using traditional methods. GA can be applied for nonlinear programming like traveling salesman problem, minimum spanning tree, scheduling problem and many others [10]. Using a GA for difficult scheduling problems enables relatively arbitrary constraints and objectives to be incorporated painlessly into a single optimization method.…”

Comparative Analysis of Three Tank Process using Soft Computing Techniques

“…Genetic algorithms (GAs) can be applied to the process controllers for their optimization using natural operators viz. mutation and crossover [7]. Although GAs provides good solution but they do not keep information about the best solution in the whole community.…”

Optimal Path Finding Method Study Based on Stochastic Travel Time

“…Classic shortest path finding methods, such as Dijkstra's algorithm, Floyd-Warshall, Bellman-Ford, A * and so on, are not suitable to be used here. Some heuristic methods have been adopted to resolve the optimal problem [15][16][17]. Based on previous research, GA-based routing algorithm has been found to be more scalable and insensitive to variations in network topologies.…”

“…For resolving the problem, some improved genetic algorithms were proposed. In this paper, the improved genetic algorithms proposed in [17] will be adopted to resolve the optimal path problem. At last, an example is analyzed to illustrate the efficiency of the proposed method.…”