
Exactly Solving the Maximum Weight Independent Set Problem on Large RealWorld Graphs
One powerful technique to solve NPhard optimization problems in practic...
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Instance Scale, Numerical Properties and Design of Metaheuristics: A Study for the Facility Location Problem
Metaheuristics are known to be strong in solving largescale instances o...
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Reidentification of Humans in Crowds using Personal, Social and Environmental Constraints
This paper addresses the problem of human reidentification across nono...
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Algorithms for Floor Planning with Proximity Requirements
Floor planning is an important and difficult task in architecture. When ...
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Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem
The Quadratic Assignment Problem (QAP) is a wellknown permutationbased...
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Probing a Set of Trajectories to Maximize Captured Information
We study a trajectory analysis problem we call the Trajectory Capture Pr...
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RoundTable Group Optimization for Sequencing Problems
In this paper, a roundtable group optimization (RTGO) algorithm is pres...
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Implementation of iterative local search (ILS) for the quadratic assignment problem
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.
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