Evolutionary Game Theoretic Multi-Objective Optimization Algorithms and Their Applications

Multi-objective optimization problems require more than one objective functions to be optimized simultaneously. They are widely applied in many science fields, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conicting objectives. Most of the real world multi-objective optimization problems are NP-Hard problems. It may be too computationally costly to find an exact solution but sometimes a near optimal solution is sufficient. In these cases, Multi-Objective Evolutionary Algorithms (MOEAs) provide good approximate solutions to problems that cannot be solved easily using other techniques. However Evolutionary Algorithm is not stable due to its random nature, it may produce very different results every time it runs. This dissertation proposes an Evolutionary Game Theory (EGT) framework based algorithm (EGTMOA) that provides optimality and stability at the same time. EGTMOA combines the notion of stability from EGT and optimality from MOEA to form a novel and promising algorithm to solve multi-objective optimization problems. This dissertation studies three different multi-objective optimization applications, Cloud Virtual Machine Placement, Body Sensor Networks, and Multi-Hub Molecular Communication along with their proposed EGTMOA framework based algorithms. Experiment results show that EGTMOAs outperform many well known multi-objective evolutionary algorithms in stability, performance and runtime

Clinical efficacy of radical nephrectomy versus nephron-sparing surgery on localized renal cell carcinoma

BACKGROUND: The aim of the present study was to compare the clinical efficacy of radical nephrectomy (RN) with nephron-sparing surgery (NSS) in treating patients with localized renal cell carcinoma (RCC). METHODS: The literature search was performed in PubMed, MEDLINE Springer, Elsevier Science Direct, Cochrane Library, and Google Scholar up to December 2012. The software Review Manager 5.1 and the STATA software package v.11.0 were used for analyses. The odds ratios (ORs) and its 95% confidence interval (95% CI) were calculated for comparison. Subgroup analyses were performed based on the tumor size of RCC. RESULTS: In total, 10 studies with 10,174 RCC patients (7,050 treated with RN and 3,124 treated with NSS) were selected. The pooled estimate (OR = 1.58, 95% CI = 1.15–2.15, P = 0.004) showed a significantly lower rate of cancer-specific deaths in the patients treated with NSS compared to RN. However, no statistically significant differences were found in the rate of tumor recurrence (OR = 0.84, 95% CI = 0.67–1.06, P = 0.14) and complications (OR = 0.91, 95% CI = 0.51–1.63, P = 0.74) between the patients treated with NSS and RN. In addition, all the subgroup analyses presented consistent results with the overall analyses. CONCLUSIONS: NSS had no significantly different from RN in tumor recurrence and complications for localized RCC. However, the significantly lower rate of cancer-specific deaths supported the use of NSS not only for RCC with tumor size >4.0 cm but also for tumor sizes ≤4.0 cm compared with RN

Congestion Control for Machine-Type Communications in LTE-A Networks

Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and Core Network (CN). 3GPP has specified several mechanisms to handle the congestion caused by massive amounts of devices. However, detailed settings and strategies of them are not defined in the standards and are left for operators. In this paper, we propose two congestion control algorithms which efficiently reduce the congestion. Simulation results demonstrate that the proposed algorithms can achieve 20~40% improvement regarding accept ratio, overload degree and waiting time compared with those in LTE-A

Impacts of S1 and X2 Interfaces on eMBMS Handover Failure: Solution and Performance Analysis

In evolved Multimedia Broadcast/Multicast Service (eMBMS), service continuity enables users move from one cell to another without interrupting eMBMS service. Unlike traditional handover in unicast transmission, a UE can receive eMBMS service in either unicast or multicast mode. In this paper, we point out a new handover failure problem in eMBMS due to the miss of rekeying information. We first take a close look at the new handover scenarios. We then investigate the problem by using comprehensive mathematical models. Our models provide insights on the new handover problem and introduce theoretical guidelines for mobile operators to design and optimize their networks without wide deployment to save cost and time. Moreover, we propose a solution to combat against the handover failure. Both the simulation and analytical results demonstrate that the impacts of the eMBMS handover failure are reduced significantly. In this paper, we present a systematic way to investigate the handover failure problem in eMBMS

Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection

Out-of-distribution (OOD) detection is an important task to ensure the reliability and safety of deep learning and the discriminator models outperform others for now. However, the feature extraction of the discriminator models must compress the data and lose certain information, leaving room for bad cases and malicious attacks. In this paper, we provide a new assumption that the discriminator models are more sensitive to some subareas of the input space and such perceptron bias causes bad cases and overconfidence areas. Under this assumption, we design new detection methods and indicator scores. For detection methods, we introduce diffusion models (DMs) into OOD detection. We find that the diffusion denoising process (DDP) of DMs also functions as a novel form of asymmetric interpolation, which is suitable to enhance the input and reduce the overconfidence areas. For indicator scores, we find that the features of the discriminator models of OOD inputs occur sharp changes under DDP and use the norm of this dynamic change as our indicator scores. Therefore, we develop a new framework to combine the discriminator and generation models to do OOD detection under our new assumption. The discriminator models provide proper detection spaces and the generation models reduce the overconfidence problem. According to our experiments on CIFAR10 and CIFAR100, our methods get competitive results with state-of-the-art methods. Our implementation is available at https://github.com/luping-liu/DiffOOD

Qubit Mapping Toward Quantum Advantage

Qubit Mapping is a pivotal stage in quantum compilation flow. Its goal is to convert logical circuits into physical circuits so that a quantum algorithm can be executed on real-world non-fully connected quantum devices. Qubit Mapping techniques nowadays still lack the key to quantum advantage, scalability. Several studies have proved that at least thousands of logical qubits are required to achieve quantum computational advantage. However, to our best knowledge, there is no previous research with the ability to solve the qubit mapping problem with the necessary number of qubits for quantum advantage in a reasonable time. In this work, we provide the first qubit mapping framework with the scalability to achieve quantum advantage while accomplishing a fairly good performance. The framework also boasts its flexibility for quantum circuits of different characteristics. Experimental results show that the proposed mapping method outperforms the state-of-the-art methods on quantum circuit benchmarks by improving over 5% of the cost complexity in one-tenth of the program running time. Moreover, we demonstrate the scalability of our method by accomplishing mapping of an 11,969-qubit Quantum Fourier Transform within five hours

ASA: Adaptive VNF Scaling Algorithm for 5G Mobile Networks

5G mobile networks introduce Virtualized Network Functions (VNFs) to provide flexible services for incoming huge mobile data traffic. Compared with fixed capacity legacy network equipment, VNFs can be scaled in/out to adjust system capacity. However, hardware-based legacy network equipment is designed dedicatedly for its purpose so that it is more efficient in terms of unit cost. One challenge is to best use VNF resources and to balance the traffic between legacy network equipment and VNFs. To address this challenge, we first formulate the problem as a cost-performance tradeoff, where both VNF resource cost and system performance are quantified. Then, we propose an adaptive VNF scaling algorithm to balance the tradeoff. We derive the suitable VNF instances to handle data traffic with minimizing cost. Through extensive simulations, the adaptive algorithm is proven to provide good performance