The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Min Huang - One of the best experts on this subject based on the ideXlab platform.
-
a multi agent evolutionary algorithm based qos unicast routing and wavelength assignment scheme
International Conference on Natural Computation, 2013Co-Authors: Junling Shi, Xingwei Wang, Min HuangAbstract:In this paper, a QoS (Quality of Service) unicast routing and wavelength assignment scheme in IP/DWDM (Dense Wavelength Division Multiplexing) optical Internet is proposed. To solve the routing problem and wavelength assignment, MEA (Multi-Agent Evolutionary Algorithm) and JFF (Joint First Fit) are used in the scheme. To realize the goal of achieving or approaching the Pareto Optimum under Nash equilibrium for the provider utility and the user utility, a fair pricing method is devised by applying the principles in microeconomics and game theory. Probability theory and fuzzy mathematics are also employed to solve the uncertainty in the link state parameters and the inflexible QoS. The simulation results demonstrate good performance of the scheme.
-
a beehive algorithm based qos unicast routing scheme with abc supported
APPT'07 Proceedings of the 7th international conference on Advanced parallel processing technologies, 2007Co-Authors: Xingwei Wang, Guang Liang, Min HuangAbstract:In this paper, a QoS unicast routing scheme with ABC supported is proposed based on beehive algorithm. It deals with inaccurate network status information and imprecise user QoS requirement, introduces edge bandwidth pricing, edge evaluation and path evaluation, and tries to find a QoS unicast path with Pareto Optimum under Nash equilibrium on both the network provider utility and the user utility achieved or approached.
-
Genetic Algorithm and Pareto Optimum Based QoS Multicast Routing Scheme in NGI
2006 International Conference on Computational Intelligence and Security, 2006Co-Authors: Xingwei Wang, Min HuangAbstract:In this paper, a QoS (quality of service) multicast routing scheme in NGI (next generation Internet) is proposed based on genetic engineering and microeconomics. It can not only deal with network status inaccuracy, but also help prevent network overload and meet with intra-group fairness, trying to find a multicast routing tree with bandwidth, delay, delay jitter and error rate satisfaction degree, bandwidth availability degree and fairness degree achieved or approached Pareto Optimum
-
a microeconomics based fuzzy qos unicast routing scheme in ngi
Embedded and Ubiquitous Computing, 2005Co-Authors: Xingwei Wang, Meijia Hou, Junwei Wang, Min HuangAbstract:Due to the difficulty on exact measurement and expression of NGI (Next-Generation Internet) network status, the necessary QoS routing information is fuzzy. With the gradual commercialization of network operation, paying for network usage calls for QoS pricing and accounting. In this paper, a microeconomics-based fuzzy QoS unicast routing scheme is proposed, consisting of three phases: edge evaluation, game analysis, and route selection. It attempts to make both network provider and user utilities maximized along the found route, with not only the user QoS requirements satisfied but also the Pareto-Optimum under the Nash equilibrium on their utilities achieved.
Weihong Zhang - One of the best experts on this subject based on the ideXlab platform.
-
a min max method with adaptive weightings for uniformly spaced Pareto Optimum points
Computers & Structures, 2006Co-Authors: Weihong Zhang, Tong GaoAbstract:Abstract This work aims at obtaining uniformly spaced Pareto Optimum points in the objective space when multicriteria optimization problems are solved. An original adaptive scheme is proposed to update automatically weighting coefficients involved in the min–max method. By means of a novel bilevel approach, it is shown that with the calculation of the tangent and normal directions of the Pareto curve, Pareto Optimum points can be obtained sequentially with a uniformly spaced distribution. Meanwhile, the distance between two adjacent Pareto Optimum points is controllable depending upon the prescribed step length along the tangent direction. To validate the method, numerical bicriteria examples are solved to show its effectiveness.
-
on the Pareto Optimum sensitivity analysis in multicriteria optimization
International Journal for Numerical Methods in Engineering, 2003Co-Authors: Weihong ZhangAbstract:To analyse the trade-off relations among the set of criteria in multicriteria optimization, Pareto Optimum sensitivity analysis is systematically studied in this paper. Original contributions cover two parts: theoretical demonstrations are firstly made to validate the gradient projection method in Pareto Optimum sensitivity analysis. It is shown that the projected gradient direction evaluated at a given Pareto Optimum in the design variable space rigorously corresponds to the tangent direction of the Pareto curve/surface at that point in the objective space. This statement holds even for the change of the set of active constraints in the perturbed problem. Secondly, a new active constraint updating strategy is proposed, which permits the identification of the active constraint set change, to determine the influence of this change upon the differentiability of the Pareto curve and finally to compute directional derivatives in non-differentiable cases. This work will highlight some basic issues in multicriteria optimization. Some numerical problems are solved to illustrate these novelties.
-
Pareto Optimum sensitivity analysis in multicriteria optimization
Finite Elements in Analysis and Design, 2002Co-Authors: Weihong ZhangAbstract:Multicriteria optimization problems are addressed in this paper. The gradient projection method originally used as a sort of feasible direction method is further developed and extended to the Pareto Optimum sensitivity analysis. It is shown that the projected search direction defines the tangent direction of the Pareto Optimum curve in the objective space. Since this method is able to identify automatically the variation of the active constraint set due to the perturbation, discontinuous derivative cases can be efficiently dealt with. To validate the method, numerical examples are solved. A comparison of the results with those obtained by the finite difference method and tangent method shows good agreement.
Xingwei Wang - One of the best experts on this subject based on the ideXlab platform.
-
a multi agent evolutionary algorithm based qos unicast routing and wavelength assignment scheme
International Conference on Natural Computation, 2013Co-Authors: Junling Shi, Xingwei Wang, Min HuangAbstract:In this paper, a QoS (Quality of Service) unicast routing and wavelength assignment scheme in IP/DWDM (Dense Wavelength Division Multiplexing) optical Internet is proposed. To solve the routing problem and wavelength assignment, MEA (Multi-Agent Evolutionary Algorithm) and JFF (Joint First Fit) are used in the scheme. To realize the goal of achieving or approaching the Pareto Optimum under Nash equilibrium for the provider utility and the user utility, a fair pricing method is devised by applying the principles in microeconomics and game theory. Probability theory and fuzzy mathematics are also employed to solve the uncertainty in the link state parameters and the inflexible QoS. The simulation results demonstrate good performance of the scheme.
-
a beehive algorithm based qos unicast routing scheme with abc supported
APPT'07 Proceedings of the 7th international conference on Advanced parallel processing technologies, 2007Co-Authors: Xingwei Wang, Guang Liang, Min HuangAbstract:In this paper, a QoS unicast routing scheme with ABC supported is proposed based on beehive algorithm. It deals with inaccurate network status information and imprecise user QoS requirement, introduces edge bandwidth pricing, edge evaluation and path evaluation, and tries to find a QoS unicast path with Pareto Optimum under Nash equilibrium on both the network provider utility and the user utility achieved or approached.
-
Genetic Algorithm and Pareto Optimum Based QoS Multicast Routing Scheme in NGI
2006 International Conference on Computational Intelligence and Security, 2006Co-Authors: Xingwei Wang, Min HuangAbstract:In this paper, a QoS (quality of service) multicast routing scheme in NGI (next generation Internet) is proposed based on genetic engineering and microeconomics. It can not only deal with network status inaccuracy, but also help prevent network overload and meet with intra-group fairness, trying to find a multicast routing tree with bandwidth, delay, delay jitter and error rate satisfaction degree, bandwidth availability degree and fairness degree achieved or approached Pareto Optimum
-
a microeconomics based fuzzy qos unicast routing scheme in ngi
Embedded and Ubiquitous Computing, 2005Co-Authors: Xingwei Wang, Meijia Hou, Junwei Wang, Min HuangAbstract:Due to the difficulty on exact measurement and expression of NGI (Next-Generation Internet) network status, the necessary QoS routing information is fuzzy. With the gradual commercialization of network operation, paying for network usage calls for QoS pricing and accounting. In this paper, a microeconomics-based fuzzy QoS unicast routing scheme is proposed, consisting of three phases: edge evaluation, game analysis, and route selection. It attempts to make both network provider and user utilities maximized along the found route, with not only the user QoS requirements satisfied but also the Pareto-Optimum under the Nash equilibrium on their utilities achieved.
Enrique Lopez Droguett - One of the best experts on this subject based on the ideXlab platform.
-
a multi objective approach for solving a replacement policy problem for equipment subject to imperfect repairs
Applied Mathematical Modelling, 2020Co-Authors: Rafael Valenca Azevedo, Marcio Das Chagas Moura, Isis Didier Lins, Enrique Lopez DroguettAbstract:Abstract This paper proposes a multi-objective approach to model a replacement policy problem applicable to equipment with a predetermined period of use (a planning horizon), which may undergo critical and non-critical failures. Corrective replacements and imperfect repairs are taken to restore the system to operation respectively when critical and non-critical failures occur. Generalized Renewal Process (GRP) is used to model imperfect repairs. The proposed model supports decisions on preventive replacement intervals and the number of spare parts purchased at the beginning of the planning horizon. A Multi-Objective Genetic Algorithm (MOGA) coupled with discrete event simulation (DES) is proposed to provide a set of solutions (Pareto-Optimum set) committed to the different objectives of a maintenance manager in the face of a replacement policy problem, that is, maintenance cost, rate of occurrence of failures, unavailability, and investment on spare parts. The proposed MOGA is validated by an application example against the results obtained via the exhaustive approach. Moreover, examples are presented to evaluate the behavior of objective functions on Pareto set (trade-off analysis) and the impact of the repair effectiveness on the decision making.
N Narimanzadeh - One of the best experts on this subject based on the ideXlab platform.
-
reliability based robust Pareto design of linear state feedback controllers using a multi objective uniform diversity genetic algorithm muga
Expert Systems With Applications, 2010Co-Authors: Ali Jamali, Amir Hajiloo, N NarimanzadehAbstract:In this paper, fuzzy threshold values, instead of crisp threshold values, have been used for optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a single inverted pendulum having parameters with probabilistic uncertainties. The objective functions that have been considered are, namely, the normalized summation of rising time and overshoot of cart (S"R"O-C) and the normalized summation of rising time and overshoot of pendulum (S"R"O-P) in the deterministic approach. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto Optimum design of linear state feedback controllers for single inverted pendulum problem. In this way, Pareto front of Optimum controllers is first obtained for the nominal deterministic single inverted pendulum using the conflicting objective functions in time domain. Such Pareto front is then obtained for single inverted pendulum having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Monte Carlo simulation (MCS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA with fuzzy threshold values includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some Optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.
-
Pareto Optimum design of robust controllers for systems with parametric uncertainties
2008Co-Authors: Amir Hajiloo, N Narimanzadeh, Ali MoeiniAbstract:The development of high-performance controllers for various complex problems has been a major research activity among the control engineering practitioners in recent years. In this way, synthesis of control policies have been regarded as optimization problems of certain performance measures of the controlled systems. A very effective means of solving such Optimum controller design problems is genetic algorithms (GAs) and other evolutionary algorithms (EAs) (Porter & Jones, 1992; Goldberg, 1989). The robustness and global characteristics of such evolutionary methods have been the main reasons for their extensive applications in off-line Optimum control system design. Such applications involve the design procedure for obtaining controller parameters and/or controller structures. In addition, the combination of EAs or GAs with fuzzy or neural controllers has been reported in literature which, in turn, constitutionally formed intelligent control scheme (Porter et al., 1994; Porter & Nariman-zadeh, 1995; Porter & Nariman-zadeh, 1997). The robustness and global characteristics of such evolutionary methods have been the main reasons for their extensive applications in off-line Optimum control system design. Such applications involve the design procedure for obtaining controller parameters and/or controller structures. In addition to the most applications of EAs in the design of controllers for certain systems, there are also much research efforts in robust design of controllers for uncertain systems in which both structured or unstructured uncertainties may exist (Wolovich, 1994). Most of the robust design methods such as μ-analysis, H2 or H∞ design are based on different normbounded uncertainty (Crespo, 2003). As each norm has its particular features addressing different types of performance objectives, it may not be possible to achieve all the robustness issues and loop performance goals simultaneously. In fact, the difficult mixed norm-control methodology such as H2/ H∞ has been proposed to alleviate some of the issue of meeting different robustness objectives (Baeyens & Khargonekar, 1994). However, these are based on the worst case scenario considering in the most possible pessimistic value of the performance for a particular member of the set of uncertain models (Savkin et al., 2000). Consequently, the performance characteristics of such norm-bounded uncertainties robust designs often degrades for the most likely cases of uncertain models as the likelihood of the O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m
-
frequency based reliability Pareto Optimum design of proportional integral derivative controllers for systems with probabilistic uncertainty
Proceedings of the Institution of Mechanical Engineers Part I: Journal of Systems and Control Engineering, 2007Co-Authors: N Narimanzadeh, Ali Jamali, Amir HajilooAbstract:AbstractA reliability-based approach for the Pareto Optimum design of proportional-integral-derivative (PID) controllers for systems with probabilistic uncertainty is presented. In this way, some non-dominated Optimum PID controllers in the Pareto sense are found using four non-commensurable objective functions in frequency domain based on stochastic behaviour of a system with parametric uncertainties. Such conflicting objective functions are namely the probability of instability, the probability of failure to a desired frequency response, the variability of frequency response about deterministic behaviour, and the value of degree of stability from the Nyquist diagram's percentiles. The first three objective functions have to be minimized whilst the last has to be maximized simultaneously. It is shown that multi-objective Pareto optimization of such robust PID controllers using a recently developed diversity-preserving mechanism genetic algorithm unveils some very important and informative trade-offs amon...
