基于绝对贪心和预期效率的0-1背包问题优化

With analysis and research the traditional theory of solving knapsack problem, and then to optimize enigmatical knapsack problems, this paper proposed a new algorithm based on the absolute greedy and expected efficiency strategy. Through the three sets of simulation experiments, it shows that the algorithm can solve a class of knapsack problems and it is superior to greedy algorithm, backtracking algorithm, dynamic programming algorithm, branch and bound algorithm. The convergence speed is ten times as the artificial glowworm swam algorithm by comparing with these two algorithms. Finally, it analyzed discrete degree of data and determined an adaptive scope of the algorithm.

Optimization algorithm of 0-1 knapsack problem based on absolute greedy and expected efficiency

This paper proposed an effective modified cuckoo search(MCS) algorithm based on stochastic local search method to solve engineering structural design optimization problems. The algorithm used the inertia weight to balance the exploration and exploitation ability of algorithm. And it introduced the stochastic local search technique to improve the convergence speed of CS algorithm. The proposed algorithm was applied to solve two engineering structural design optimization problems. The result shows that MCS is of better or competitive performances when it compares with other existing optimization methods.