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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 12

Novel human learning optimization algorithm for multidimensional knapsack problem

Zhang Yipeng
Liu Yong
Ma Liang
Management School, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0127

Abstract

Aiming at the problems of low accuracy and poor stability of the current algorithms in solving multi-dimensional knapsacks, especially the inability to effectively solve super-large-scale arithmetic cases, this paper proposes a new type of human learning optimization algorithm. Firstly, the noval human learning algorithm uses a hash function based on the memory theory in cognitive psychology to represent the memory behaviour of human beings in the learning process, avoiding repeated searches and improving the algorithm's search group diversity; Second, the algorithm uses the contrastive cognition theory from cognitive psychology to adaptively adjust the learning operator selection strategy; Finally, the algorithm uses a variable neighborhood search operation to enhance the algorithm's local search capability. We conducted numerical experiments using a standardized test dataset of a total of 76 multidimensional knapsack problems that cover different scales such as small, medium, large, and very large. In our experiments, we compared the new algorithm with binary particle swarm algorithms, genetic algorithms, human learning algorithms, and human learning algorithms that incorporate the psychology of learning. The results show that the new algorithm is able to solve the four scale instances efficiently. Compared with other algorithms, the new algorithm has higher accuracy in finding the optimum and better stability. In addition, we analyze the three proposed optimization strategies to test their effectiveness in improving the algorithm's search performance.

Foundation Support

教育部人文社会科学研究青年基金资助项目(21YJC630087)
上海市哲学社会科学规划课题(2019BGL014)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0127
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 12

Publish History

[2024-09-06] Accepted Paper

Cite This Article

张翼鹏, 刘勇, 马良. 多维背包问题的新型人类学习优化算法 [J]. 计算机应用研究, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0127. (Zhang Yipeng, Liu Yong, Ma Liang. Novel human learning optimization algorithm for multidimensional knapsack problem [J]. Application Research of Computers, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0127. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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