Quantum-aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming
Abstract
Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circumventing this problem. In this context, the so-called Evolutionary Quantum Pareto Optimization (EQPO) algorithm has been proposed, which is capable of identifying most of the optimal routes at a near-polynomial complexity versus the number of nodes. As a benefit, we improve both the the EQPO algorithm by introducing a back-tracing process. We also demonstrate that the improved algorithm, namely the Back-Tracing-Aided EQPO (BTA-EQPO) algorithm, imposes a negligible complexity overhead, while substantially improving our performance metrics, namely the relative frequency of finding all Pareto-optimal solutions and the probability that the Pareto-optimal solutions are indeed part of the optimal Pareto front
Cite
@article{arxiv.1804.02387,
title = {Quantum-aided Multi-Objective Routing Optimization Using Back-Tracing-Aided Dynamic Programming},
author = {Dimitrios Alanis and Panagiotis Botsinis and Zunaira Babar and Hung Viet Nguyen and Daryus Chandra and Soon Xin Ng and Lajos Hanzo},
journal= {arXiv preprint arXiv:1804.02387},
year = {2018}
}
Comments
Accepted in Transactions on Vehicular Technology