A Hybrid Optimization GNA Algorithm for the Quadratic Assignment Problem Solving

Abstract

The quadratic assignment problem (QAP) was considered one of the most significant combinatorial optimization problems due to its variant and substantial applications in real life such as scheduling, production, computer manufacture, chemistry, facility location, communication, and other fields. QAP is an NP-hard problem that is impossible to be solved in polynomial time when the problem size increases, hence heuristic and metaheuristic approaches are utilized for solving the problem instead of exact methods. Optimization plays a significant role in easing this problem. In this paper, we will provide a solution to optimize QAP. In the QAP problem, there is a total of facilities (departments, company’s,…etc.) that must be located to minimize the flow (amount of material to be exchanged). Thus, the objective function is composed by multiplying both distances between the locations and the flow among these facilities. Global Neighborhood (GNA) Algorithm will be used to optimize the QAP problem, and the solution will also be compared to the well-known Genetic Algorithm (GA).