405 research outputs found
Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel
This paper considers an artificial noise (AN)-aided transmit design for
multi-user MIMO systems with integrated services. Specifically, two sorts of
service messages are combined and served simultaneously: one multicast message
intended for all receivers and one confidential message intended for only one
receiver and required to be perfectly secure from other unauthorized receivers.
Our interest lies in the joint design of input covariances of the multicast
message, confidential message and artificial noise (AN), such that the
achievable secrecy rate and multicast rate are simultaneously maximized. This
problem is identified as a secrecy rate region maximization (SRRM) problem in
the context of physical-layer service integration. Since this bi-objective
optimization problem is inherently complex to solve, we put forward two
different scalarization methods to convert it into a scalar optimization
problem. First, we propose to prefix the multicast rate as a constant, and
accordingly, the primal biobjective problem is converted into a secrecy rate
maximization (SRM) problem with quality of multicast service (QoMS) constraint.
By varying the constant, we can obtain different Pareto optimal points. The
resulting SRM problem can be iteratively solved via a provably convergent
difference-of-concave (DC) algorithm. In the second method, we aim to maximize
the weighted sum of the secrecy rate and the multicast rate. Through varying
the weighted vector, one can also obtain different Pareto optimal points. We
show that this weighted sum rate maximization (WSRM) problem can be recast into
a primal decomposable form, which is amenable to alternating optimization (AO).
Then we compare these two scalarization methods in terms of their overall
performance and computational complexity via theoretical analysis as well as
numerical simulation, based on which new insights can be drawn.Comment: 14 pages, 5 figure
On Insurer Portfolio Optimization. An Underwriting Risk Model
Multicriteria portfolio optimization started with the Markowitz mean-variance model (Markowitz 1952, 1959). This model assumes that the goal of an average or standard investor is to maximize the unknown return on investment. In this paper we propose a risk model related to insurance industry. The optimality criteria we propose for insurerâs portfolio optimization are based on the well-known Markowitz model, yet imposing scalarization on the components of the objective function.portfolio optimization, underwriting risk, scalarization.
Evaluation of scalarization methods and NSGA-II/SPEA2 genetic algorithms for multi-objective optimization of green supply chain design
This paper considers supply chain design in green logistics. We formulate the choice of an environmentally conscious chain design as a multi-objective optimization (MOO) problem and approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods as well as with two popular genetic algorithms, NSGA-II and SPEA2. We extend an existing case study of green supply chain design in the South Eastern Europe region by optimizing simultaneously costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. The results show that in the considered case the scalarization methods outperform genetic algorithms in finding efficient solutions and that the CO2 and PM emissions can be lowered by accepting a marginal increase of costs over their global minimum
Optimal dimensional synthesis of force feedback lower arm exoskeletons
This paper presents multi-criteria design optimization of parallel mechanism based force feedback exoskeletons for human forearm and wrist. The optimized devices are aimed to be employed as a high fidelity haptic interfaces. Multiple design objectives are discussed and classified for the devices and the optimization problem to study the trade-offs between these criteria is formulated. Dimensional syntheses are performed for optimal global kinematic and dynamic performance, utilizing a Pareto front based framework, for two spherical parallel mechanisms that satisfy the ergonomic necessities of a human forearm and wrist. Two optimized mechanisms are compared and discussed in the light of multiple design criteria. Finally, kinematic structure and dimensions of an optimal exoskeleton are decided
Multiobjective approach to optimal control for a dengue transmission model
During the last decades, the global prevalence of dengue progressed
dramatically. It is a disease which is now endemic in more than one hundred
countries of Africa, America, Asia and the Western Pacific. This study
addresses a mathematical model for the dengue disease transmission and finding
the most effective ways of controlling the disease. The model is described by a
system of ordinary differential equations representing human and vector
dynamics. Multiobjective optimization is applied to find the optimal control
strategies, considering the simultaneous minimization of infected humans and
costs due to insecticide application. The obtained results show that
multiobjective optimization is an effective tool for finding the optimal
control. The set of trade-off solutions encompasses a whole range of optimal
scenarios, providing valuable information about the dynamics of infection
transmissions. The results are discussed for different values of model
parameters.FCOMP-01-0124-FEDER-02889
Scalarization and sensitivity analysis in Vector Optimization. The linear case.
In this paper we consider a vector optimization problem; we present some scalarization techniques for finding all the vector optimal points of this problem and we discuss the relationships between these methods. Moreover, in the linear case, the study of dual variables is carried on by means of sensitivity analysis and also by a parametric approach. We also give an interpretation of the dual variables as marginal rates of substitution of an objective function with respect to another one, and of an objective function with respect to a constraint.Vector Optimization, Image Space, Separation, Scalarization, Shadow Prices
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