基于优化粒子群的货物装箱管理方案  

Cargo Box Management Scheme Based on Optimized Particle Swarm

作　　者：胥珠峰 XU Zhufeng(Business School,Hohai University,Nanjing 210098)

机构地区：[1]河海大学商学院,南京210098

出　　处：《计算机与数字工程》2018年第8期1520-1524,1626,共6页Computer & Digital Engineering

基　　金：国家自然科学基金项目(编号:61272543);国家科技支撑计划项目(编号:2013BAB06B04)资助

摘　　要：面对货物装箱管理组合优化的复杂性问题,该研究在固定的箱体内依据最大体积装载率构建目标函数,利用剩余矩形算法实现同类块的装载策略。在分析简化粒子群的基础上,给出了编码方式和基本操作流程,重点引入了小生境技术(NT)与混合蛙跳算法(SFLA)对粒子群算法进行优化,通过改变种群拓扑结构、更新算法的优化公式,提升了种群多样性与全局寻优能力。实验的仿真结果表明:该研究提出的优化粒子群算法不仅提高了固定箱体内的空间利用率,与其他算法相比,平均求解质量较其中最优解提升0.39%,并且较采用简化粒子群算法的平均求解质量高2.78%,算例对比结果验证了优化粒子群算法的有效性与优越性。In the face of the complexity of combinatorial optimization of cargo packing management,this paper constructs the objective function according to the maximum volume loading rate in the fixed box,and uses the remaining rectangle algorithm to realize the loading strategy of the same kind of block. Based on the analysis of simplified particle swarmes,the coding scheme and basic operation flow are given. Focusing on the introduction of niche technology（NT）and hybrid leapfrog algorithm（SFLA）is used to optimize the particle swarm optimization algorithm. By changing the population topology,The optimization formula of the algorithm improves the population diversity and the global optimization ability. The simulation results show that the optimized particle swarm optimization algorithm not only improves the space utilization in the fixed box. Compared with other algorithms,the optimal solution quality is 0.39% higher than that of the optimal solution,and compared with the simplified particle swarm optimization The average solution quality is 2.78%,and the validity and superiority of the optimized particle swarm optimization algorithm are verified by an example.

关 键 词：粒子群算法 小生境技术 混合蛙跳算法 货物装箱 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]