Multi-group end-to-end path optimization algorithm based on genetic algorithm

This paper proposes a multi-group end-to-end path optimization method based on genetic algorithm(MEEPOGA). Under the condition of meeting the bandwidth requirements and delay requirements of data transmission, in a network with limited link capacity and given delay, MEEPOGA arranges data transmission paths for multiple groups of source nodes to destination nodes. These paths achieve the goal of minimizing overall cost while avoiding link congestion. Considering that the genetic algorithm can provide stable and efficient search in the complex problem space, we solve the above optimization problem by making appropriate improvements to the genetic algorithm. It mainly includes modification of encoding strategy, fitness function and genetic operator. At the same time, we conducted comparative experiments with other algorithms. The optimization method proposed in this paper is mainly divided into two steps: First, MEEPOGA finds a set of possible solutions for each pair of source nodes and destination nodes under the conditions of bandwidth and delay. Then the combination evaluation is carried out through the genetic algorithm to find the optimal solution. For evaluation on a collection of paths, an objective-based penalty function is proposed. Simulation experiments show that our algorithm has good performance.