A new robust Harris Hawk optimization algorithm for large quadratic assignment problems

Dokeroglu T., Ozdemir Y. S.

Neural Computing and Applications, vol.35, no.17, pp.12531-12544, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 17
  • Publication Date: 2023
  • Doi Number: 10.1007/s00521-023-08387-2
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.12531-12544
  • Keywords: Harris Hawk optimization, Metaheuristic, Quadratic assignment problem, Tabu search
  • TED University Affiliated: No

Abstract

Harris Hawk optimization (HHO) is a new robust metaheuristic algorithm proposed for the solution of large intractable combinatorial optimization problems. The hawks are cooperative birds and use many intelligent hunting techniques. This study proposes new HHO algorithms for solving the well-known quadratic assignment problem (QAP). Large instances of the QAP have not been solved exactly yet. We implement HHO algorithms with robust tabu search (HHO-RTS) and introduce new operators that simulate the actions of hawks. We also developed an island parallel version of the HHO-RTS algorithm using the message passing interface. We verify the performance of our proposed algorithms on the QAPLIB benchmark library. One hundred and twenty-five of 135 problems are solved optimally, and the average deviation of all the problems is observed to be 0.020%. The HHO-RTS algorithm is a robust algorithm compared to recent studies in the literature.