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中文摘要：
针对二进制粒子群算法在求解大规模多维背包问题时存在迭代次数过多、精度不高的不足,提出一种改进的二进制粒子群算法,新算法利用种群个体极值的平均信息和粒子的个体极值决定粒子当前取值的概率,使粒子可以充分利用整个种群的信息,避免算法陷入局部极值,并利用贪婪算法对进化过程中的不可行解进行修复,对背包资源利用不足的可行解进行修正.通过对典型多维背包问题的仿真实验和与其它算法的比较,表明算法有良好的全局优化能力和较好的收敛速度.
英文摘要：
Binary particle swarm optimization takes too much time and solves imprecisely for large-scaled multidimensional knapsack problem, a modified binary particle swarm opti- mization is proposed in this paper to overcome this shortcoming. The new algorithm uses the values of the average individual best position and the individual best position depend on the probability of the position vector, makes each particle use the whole swarm information effectively to avoid local optima. During the evolution process, it uses the greedy algorithm repairs the infeasible solution and rectify knapsack resources with insufficient use. Simulated tests of multidimensional knapsack problem and comparisons with other algorithms show the algorithm has strong global optimization ability and a good speed of convergence.
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