文件名称:GoodsAllocatingProblemwithMultiAimsbasedonTheHybri
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多目标货物配装问题是一个复杂的组合优化问题，属于NP难问题，本文用混合粒子群算法求解多目标货物配装问题。混合粒子群算法在基本粒子群算法的基础上，通过引进遗传算法中的交叉和变异的策略，避免了陷入局部最优，加快了达到全局最优的收敛速度。此外，本文提出用权重系数来平衡各目标使各目标都能达到相对较优的效果。-Multi-objective loading of goods is a complicated combinatorial optimization problems are NP hard problems, this paper, hybrid particle swarm algorithm to solve multi-objective problem loading cargo. Hybrid Particle Swarm Algorithm in elementary particle swarm optimization based on genetic algorithm through the introduction of crossover and mutation of the strategy to avoid a fall into local optimum, global optimum to achieve accelerated convergence. In addition, this paper, the weight factor used to balance the various objectives so that the objectives can be achieved relatively better results.
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Goods Allocating Problem with Multi-Aims based on The Hybrid Particle Swarm Algorithm
.....................................................................................\crossover.m
.....................................................................................\fitness.m
.....................................................................................\HPSO.m
.....................................................................................\mutation.m
.....................................................................................\PSO.m
.....................................................................................\PSOdata.mat
.....................................................................................\PSO_con.m
.....................................................................................\PSO_conV.m
.....................................................................................\crossover.m
.....................................................................................\fitness.m
.....................................................................................\HPSO.m
.....................................................................................\mutation.m
.....................................................................................\PSO.m
.....................................................................................\PSOdata.mat
.....................................................................................\PSO_con.m
.....................................................................................\PSO_conV.m