Application of discrete Grey Wolf Algorithm in balanced transport problem

Aiming at the problem of balanced transportation decision-making, a new heuristic algorithm is proposed-discrete grey wolf optimizer (DGWO) based on Matthew effect and greedy strategy. Firstly, based on the comprehensive analysis of the decision-making elements of balanced transportation decision-making, the balanced decision-making model is established. Then, based on the mathematical mapping idea, a code conversion method is given, which can transform the continuous search space into discrete search space and change the real numbers to integers; Finally, two constraints on the balanced transportation decision-making model condition. Due to the first constrain, we use the repair and optimization algorithm based on Matthew effect to process. Then, the second constraint is processed by the repair and optimization algorithm based on the greedy strategy, which can repair and optimize the infeasible solutions into feasible solutions. Two examples are used to verify the algorithm. The example’s result shows that the DGWO algorithm is better than the genetic algorithm in terms of convergence speed or convergence accuracy, which proves the effectiveness, correctness, and superiority of DGWO.