Open AccessImproved Hunting Search Algorithm for the Quadratic Assignment Problem
Author(s) -
Amine Agharghor,
Mohammed Essaid Riffi,
Fayçal Chebihi
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v14.i1.pp143-154
Subject(s) - metaheuristic , mathematical optimization , parallel metaheuristic , local search (optimization) , set (abstract data type) , quadratic assignment problem , combinatorial optimization , algorithm , search algorithm , computer science , optimization problem , guided local search , mathematics , meta optimization , programming language
Nowadays, the metaheuristics are the most studied methods used to solve the hard optimization problems. Hunting Search algorithm is a metaheuristic inspired by the method of group hunting of predatory animals like wolves. Created for solving continuous optimization problems, recently, it is adapted and evaluated to solve hard combinatorial optimization problems. This paper proposes an improved hunting search algorithm to solve the quadratic assignment problem. No local search method is used. To evaluate the performances of this work, the improved Hunting Search is checked on a set of 36 instances of QAPLib and it outperforms the results obtained by the well-known metaheuristics.