Optimization Of Utility Functions Research Articles

A Deep Reinforcement Learning-Based Offloading Scheme for Multi-Access Edge Computing-Supported eXtended Reality Systems

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption.

Adaptive QoS-aware multipath congestion control for live streaming

Nowadays, the popularization of live streaming bring challenges in providing customized Quality of Service (QoS) and satisfying diverse Quality of Experience (QoE) requirements. Multipath TCP (MPTCP), designed for bandwidth and reliability in aggregating transmission, has the potential to improve live streaming performance with multiple simultaneously transmitting paths. However, the existing multipath congestion control algorithms (CCAs) of MPTCP fail to adapt to diverse network environments and different QoS requirements. We study the problem with extensive experiments to observe the limitations of current multipath CCAs. To tackle these problems and support stable and customized live streaming, we propose ACCeSS, an adaptive QoS-aware multipath congestion control framework. ACCeSS is able to promptly adapt to network changes and QoS requirements with a novel control policy optimization phase. In order to adjust and stimulate improvement on the preferred performance metric, ACCeSS exploits Random Forest Regressing (RFR) method to perform QoS-specific utility function optimization. Besides, ACCeSS is implemented and deployed in a multipath live streaming system. We compare it with other multipath CCAs in the Linux kernel and evaluate their performance in both emulated and real-world networks. It is revealed that ACCeSS outperforms classic multipath CCAs and the state-of-the-art learning-based multipath CCA, with better environment adaptive capability of QoS and higher QoE for live streaming.

Multi-Objective Surface Water Resource Management Considering Conflict Resolution and Utility Function Optimization

In the present research, a multi-objective model is developed for surface water resource management in the river basin area which is connected to the lake. This model considers different components of sustainable water resource management including economic, social and environmental aspects, and simultaneously tries to resolve conflicts between different stakeholders by means of non-symmetric Nash bargaining, which is linked to the multi-objective optimization method. This study proposes a new methodology to improve Nash Conflict Resolution through finding the optimum degree of the utility function. The proposed model is examined in the Zarrineh River basin in Iran. The results show that the amount of available resources or volume of reservoirs play a significant role in determining the optimal degree of the utility function and efficiency of the proposed method in such a way that the higher amount of resources or the larger reservoirs will result in the higher optimal degree of the utility function. In the proposed multi-objective model, two different amounts of surface water inflow are considered. The first assumed amount is the long-term average flow rate and the second one is equal to 80% of the first mode, which is reduced based on the estimated impacts of climate changes. This multi-objective allocation model could supply 100 and 97.5% of the environmental demand of Lake Urmia in the first and second situations, respectively.

ANALISIS PORTOFOLIO SAHAM LQ45 MENGGUNAKAN FUNGSI UTILITAS KUADRATIK

Utility function can use to give risk preference for investors who want to get the benefits gained meets investment targets. Quadratic utility functions on optimal portfolio is strongly influenced by the expected return and standard deviation. The establishment of optimal portfolios using a quadratic utility function optimization problems. Under the settlement portfolio optimization, the necessary data is expected return, variance, and variance covariance matrix. The optimal portfolio is affected by some factors Risky less Rate, risk aversion index, and Borrow Rate. The results of settlement portfolio optimization is obtaining the utility value while the relatively large changes influencing by risk averse index.

Application of the Utility Function Method for Behavioral Organization in a Locomotion Task

The generation of a complete robotic brain for locomotion based on the utility function (UF) method for behavioral organization is demonstrated. A simulated, single-legged hopping robot is considered, and a two-stage process is used for generating the robotic brain. First, individual behaviors are constructed through artificial evolution of recurrent neural networks (RNNs). Thereafter, a behavioral organizer is generated through evolutionary optimization of utility functions. Two systems are considered: a simplified model with trivial dynamics, as well as a model using full newtonian dynamics. In both cases, the UF method was able to generate an adequate behavioral organizer, which allowed the robot to perform its primary task of moving through an arena, while avoiding collisions with obstacles and keeping the batteries sufficiently charged. The results for the simplified model were better than those for the dynamical model, a fact that could be attributed to the poor performance of the individual behaviors (implemented as RNNs) during extended operation.

An adaptive framework for addressing fairness issues in wireless networks

We present a framework for designing optimal policies addressing resource allocation problems in wireless networks. We consider a general utility function optimization objective. Specific choices of the utility functions lead to policies that satisfy several well-known fairness criteria, e.g. max– min and proportional- fair. Traditional approaches for solving these nonlinear optimization problems in an off-line manner, lead to nonadaptive policies that usually rely on system parameters, which may not be known a priory. Within our framework the development of such policies is based on the adaptive employment of policies that solve linear optimization problems. In several situations the development of policies for these linear problems is fairly simple and depends minimally on system parameters. Subsequently, we apply this method to three specific wireless resource allocation problems. In particular we consider wireless fading channel systems and provide optimal policies for (a) uplink optimal power allocation for constant bit rate connections, (b) uplink optimal average throughput allocation, and (c) downlink optimal scheduling with limited transmission rate capabilities over multiple fading channels.

A method for behavioural organization for autonomous robots based on evolutionary optimization of utility functions

A method for behavioural organization for autonomous robots based on evolutionary optimization of utility functions

A method for behavioural organization for autonomous robots based on evolutionary optimization of utility functions

A method for behavioural organization for autonomous robots, based on evolutionary optimization of utility functions, is introduced and illustrated through several simulation examples. The method is shown to be efficient in generating behavioural organization systems that are both flexible and robust to noise. With this method, the amount of hand-tuning of parameters is minimized and, in principle, the user is only required to define fitness functions for the behaviours directly related to the task of the robot. The utility functions representing the beliefs and intentions of the robot are, in general, obtained through evolutionary optimization. However, if desired, the user also has the freedom to specify utility functions by hand.

Quality Utility—A Compromise Programming Approach to Robust Design

In robust design, associated with each quality characteristic, the design objective often involves multiple aspects such as “bringing the mean of performance on target” and “minimizing the variations.” Current ways of handling these multiple aspects using either the Taguchi’s signal-to-noise ratio or the weighted-sum method are not adequate. In this paper, we solve bi-objective robust design problems from a utility perspective by following upon the recent developments on relating utility function optimization to a Compromise Programming (CP) method. A robust design procedure is developed to allow a designer to express his/her preference structure of multiple aspects of robust design. The CP approach, i.e., the Tchebycheff method, is then used to determine the robust design solution which is guaranteed to belong to the set of efficient solutions (Pareto points). The quality utility at the candidate solution is represented by means of a quadratic function in a certain sense equivalent to the weighted Tchebycheff metric. The obtained utility function can be used to explore the set of efficient solutions in a neighborhood of the candidate solution. The iterative nature of our proposed procedure will assist decision making in quality engineering and the applications of robust design.

Utility optimization when the utility function is virtually unknown

In this paper a surrogate to traditional utility optimization is presented. This approach is based on multiple criteria decision making techniques through a theorem which connects utility function optimization with compromise programming. Apart from common assumptions in the literature the only specific assumption underlying the approach seems realistic, and is markedly related to traditional analysis.

A theorem connecting utility function optimization and compromise programming

The aim of this paper is to determine conditions under which the Lagrangian maximum of a utility function and compromise programming lead to close solutions.