返回
Binary artificial algae algorithm for multidimensional knapsack problems
DOI:10.1016/j.asoc.2016.02.027.png)
摘要
En 中文
The multidimensional knapsack problem (MKP) is a well-known NP-hard optimization problem. Various meta-heuristic methods are dedicated to solve this problem in literature. Recently a new meta-heuristic algorithm, called artificial algae algorithm (AAA), was presented, which has been successfully applied to solve various continuous optimization problems. However, due to its continuous nature, AAA cannot settle the discrete problem straightforwardly such as MKP. In view of this, this paper proposes a binary artificial algae algorithm (BAAA) to efficiently solve MKP. This algorithm is composed of discrete process, repair operators and elite local search. In discrete process, two logistic functions with different coefficients of curve are studied to achieve good discrete process results. Repair operators are performed to make the solution feasible and increase the efficiency. Finally, elite local search is introduced to improve the quality of solutions. To demonstrate the efficiency of our proposed algorithm, simulations and evaluations are carried out with total of 94 benchmark problems and compared with other bio-inspired state-of-the-art algorithms in the recent years including MBPSO, BPSOTVAC, CBPSOTVAC, GADS, bAFSA, and IbAFSA. The results show the superiority of BAAA to many compared existing algorithms. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Artificial algae algorithm
Multidimensional knapsack problem
Pseudo-utility ratio
Elite local search
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Significance of epididymal and ductal anomalies associated with undescended testis: Study in 652 cases
Urology
IF0
Teacher support of student engagement in early childhood: embracing ambivalence through playworlds
Early Years
IF0
A dynamic programming based reduction procedure for the multidimensional 0-1 knapsack problem多维0-1背包问题的基于动态规划的归约过程

