A stagnation-aware cooperative parallel breakout local search algorithm for the quadratic assignment problem

2017 ◽  
Vol 103 ◽  
pp. 105-115 ◽  
Author(s):  
Yagmur Aksan ◽  
Tansel Dokeroglu ◽  
Ahmet Cosar
Keyword(s):  
Local Search ◽  
Assignment Problem ◽  
Search Algorithm ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment ◽  
Local Search Algorithm

A Neighborhood Combining Approach in GRASP's Local Search for Quadratic Assignment Problem Solutions

2018 ◽  
Vol 22 (1) ◽  
Author(s):  
Erika Granillo Martinez ◽  
Rogelio González Velázquez ◽  
María Beatriz Bernabé Loranca ◽  
José Luis Martínez Flores
Keyword(s):  
Local Search ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment

scholarly journals Implementation of iterative local search (ILS) for the quadratic assignment problem

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Local Search ◽  
Open Source ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Source Code ◽  
Population Based ◽  
Quadratic Assignment ◽  
Exact Methods ◽  
Open Source Code

<p>The quadratic assignment problem (QAP) is one of the hardest NPhard problems and problems with a dimension of 20 or more can be difficult to solve using exact methods. The QAP has a set of facilities and a set of locations. The goal is to assign each facility to a location such that the product of the flow between pairs of facilities and the distance between them are minimized. Sometimes there is also a cost associated with assigning a facility to a location. In this work, I solve the QAP using a population based iterative local search with open source code in C++. Results show that the code is able to solve all nug instances to optimality, thereby proving that the algorithm is capable of solving larger problems for which optimum solutions are not known.</p>

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