基于改进粒子群算法的车辆配装问题求解

为解决普零货物的车辆配装问题,通过引入合并策略对标准蚁群算法进行了改进。算法中构造了和配装问题相适应的粒子,使得粒子每一维对应一个货物票号并且其取值为装载此货物的车辆编号,即一个粒子对应一个配装方案。考虑车辆的容积、载重等约束条件,在计算粒子适应度时引入了超载惩罚系数,并为改善粒子局部优化能力提出了合并策略。此算法优化过程运算简单、并行,粒子根据群体和个体历史经验逐步向最优解靠近。实例验证结果表明,该算法是有效和可行的。

Cargo-loading Problem Based on Improved Particle Swarm Optimization Algorithm.

An improved particle swarm optimization(IPSO)is proposed to solve the cargo-loading problem by introducing an incorporation idea.Particle is constructed that the dimension of the particle equal to the sum of the goods and every dimension of the particle corresponded to one assigned vehicle,that is a particle represents a scheme of loading.Considering loading weight and volume,the punishment coefficient of over-loading is introduced in calculating fitness,as well as incorporation idea is proposed to improve the local optimization.This optimization algorithm is simple and parallel,and the particle closes up to the best position according to the history experience of swarm and individual in the course of evolution.Application result shows that the algorithm is available and feasible.