中文摘要：

提出了一种思想简单且可用于0-1背包问题求解的基于贪婪策略整体分布优化算法.该算法首先随机产生一个初始种群,经贪婪策略将种群变成价值相对较高的可行解,保留本次最优解;然后以最优解为中心,用柯西分布产生新的种群,经贪婪策略将新种群变成相对价值较高的可行解,再保留本次最优解,重复以上过程,达到最大迭代次数,求出问题的全局最优解;最后,对不同规模的问题进行了实验.结果表明：该算法在求解0-1背包问题上是有效的,比遗传算法、贪婪算法具有更强的寻优能力.

英文摘要：

Overall distribution optimization algorithm based on greedy strategy with simple idea, which can be used to solve large-scale 0-1 knapsack problem is presented in the paper. Firstly, an initial population is brought out randomly in the method, and according to greedy strategy, it is developed into higher valuable feasible solution and the optimum solution is reserved. Then, a new population is produced around optimum solution according to Cauchy distribution, and again it＇s developed into higher valuable feasible solution after greedy strategy and it＇s reserved. Repeat the whole solution procedure above, reach the most iteration, and seek out the globally optimal solution to the problem. Lastly, different scale problems are tested. The result shows that the method is efficient in solving 0- 1 knapsack problem, and compared to genetic algorithm and greedy strategy, it＇s more powerful to seeking for optimum.