A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.

基于遗传算法的集装箱货物装配方案研究

论文介绍了遗传算法在装配大型集装箱中的应用。首先对传统的背包问题进行了分析,指出了其中存在的问题,提出了一个以提高安全系数和最大价值为目标的更具现实意义的多目标模型。然后采用遗传算法求解,并给出了一个算法求解实例。计算表明文中采用的遗传优化算法有较高的搜索效率。