A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust...

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Publicado en:Scientific World Journal pp. 637412 - 637413
Autores principales: Tuo, Shouheng, Yong, Longquan, Deng, Fang'an
Formato: research Journal Article
Publicado: Wiley-Blackwell 2014
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.
      aug:
        au:
          Tuo, Shouheng
          Yong, Longquan
          Deng, Fang'an
        affil: School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China.
      sug:
        subj:
          Algorithms
          Artificial Intelligence
          Web Search Engines Methods
          Computer Simulation
      ab: To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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