A Binary Sine-Cosine Algorithm applied to the Knapsack problem

Hernan Pinto, Alvaro Peña, Matías Valenzuela, Andrés Fernández

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


In industry, the concept of complex systems is becoming relevant due to the diverse applications in operations research. Many of these complex problems are NP-hard and it is difficult to approach them with complete optimization techniques. The use of metaheuristics has had good results and in particular, the design of binary algorithms based on continuous metaheuristics of swarm intelligence. In this article, we apply the binarization mechanism based on the percentile concept. We apply the percentile concept to the sine-cosine algorithm (SCOA) in order to solve the multidimensional backpack problem (MKP). The experiments are designed to demonstrate the usefulness of the percentile concept in binarization. In addition, we verify the efficiency of our algorithm through reference instances. The results indicate that the binary Percentile Sine-Cosine Optimization Algorithm (BPSCOA) obtains adequate results when evaluated with a combinatorial problem such as the MKP.

Original languageEnglish
Title of host publicationArtificial Intelligence Methods in Intelligent Algorithms - Proceedings of 8th Computer Science On-line Conference 2019, Vol. 2
EditorsRadek Silhavy
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783030198091
StatePublished - 2019
Event8th Computer Science On-line Conference, CSOC 2019 - Prague, Czech Republic
Duration: 24 Apr 201927 Apr 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Conference8th Computer Science On-line Conference, CSOC 2019
Country/TerritoryCzech Republic


  • Combinatorial optimization
  • KnapSack
  • Metaheuristics
  • Percentile


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