遗传离散粒子群算法求解车辆货物配装问题

Genetic Discrete Particle Swarm Optimization Algorithm for Solving Vehicle Cargo Loading Problem

针对车辆货物配装离散组合优化问题的特点,建立了能均衡利用车辆载重和容积的数学模型。采用遗传离散粒子群算法,将遗传算法中的选择、交叉、变异操作加入到离散粒子群算法的寻优过程中,在保证粒子群多样性的前提下,改善了新一代粒子的适应能力。通过计算实例验证了遗传离散粒子群算法的有效性,并与启发式算法和基本粒子群算法进行了对比,结果表明遗传离散粒子群算法在车辆货物配装组合优化问题中具有很强的全局搜索能力,并可以获得更好的优化结果。

Aiming at the characteristics of vehicle cargo loading for discrete combinatorial optimization problem,the mathematical model was established by using balanced the vehicle load and volume.Genetic discrete particle swarm optimization algorithm was adopted,where the selecting,cross and mutation operations of genetic algorithm are imported in the optimization of discrete particle swarm optimization algorithm.It can improve the adaptability of the new generation of particles for solving vehicle cargo loading problem.The effectiveness of the genetic discrete particle swarm optimization algorithm is verified by a test example.Compared with the heuristic algorithm and the basic particle swarm optimization algorithm,the results of the experiment show that the genetic discrete particle swarm optimization algorithm has a strong global search ability in vehicle cargo loading in combinatorial optimization,and can achieve better optimization solution.