A genetic algorithm for optimal flow assignment in computer network

In this paper, we consider the optimal flow assignment problem of finding a set of link flows that satisfy the requirements and minimize the average end-to-end network delay for a given topology characterized by the capacities and costs of links. This problem can be formulated as a nonlinear programming problem. In recent years, a growing body of literature suggests the use of genetic algorithm as one of powerful heuristic search methods to deal with many hard-solving problems. For highly constrained problems, conventional genetic operators often yield illegal solutions in the sense of violation of system constraints. In this study, we present an implementation of genetic algorithms to the optimal flow assignment problem. Non-uniform mutation and arithmetical crossover are adopted to guarantee the legitimation of offspring. Simulation results show that the proposed approach performs well for this problem.