A heuristic method for solving redundancy optimization problems incomplex systems

The authors present a heuristic method for solving constrained redundancy optimization problems in complex systems. The proposed method allows excursions over a bounded infeasible region, which can alleviate the risks of being trapped at a local optimum. Computational results show that the method performs consistently better than other heuristic methods in terms of solution quality. If solution quality is of more concern and if one is willing to accept a moderate increase in computing time for better solutions, then the authors believe that this method is an attractive alternative to other heuristic methods     

Multi-path heuristic for redundancy allocation: the tree heuristic

A tree heuristic is presented for solving the general redundancy allocation problem in reliability optimization. The tree heuristic can obtain several local optima by branching off the main searching path when some criterions are satisfied. Then, the best local optima is selected for the final solution. The tree heuristic is a simple, efficient, iterative heuristic for any integer nonlinear programming problems with increasing constraint functions. Iterative heuristics are normally trapped in a local optimum. However, the tree heuristic can overcome local optima by branching the solution path. The experiments show that the proposed heuristic is very efficient in terms of solution quality, and computation time.     

A simple method for optimal redundancy allocation for complex networks

In this paper a simple method for optimum redundancy allocation for complex networks is presented. A new heuristic criterion is introduced for solving the problem by decomposing the same in two phases. After finding all minimal path sets of the system in phase I, we choose a minimal path set having the highest value of the ‘sensitivity factor’. In phase II we add one component to the stage having the largest value of the selection factor amongst the stages of the selected minimal path set. This procedure is continued until no more redundant component can be added within the available resources. The proposed method is simple, easily computerizable, fast and capable of handling problems subject to any number of constraints. The method has been illustrated by examples and results are compared with the existing methods.     

A new heuristic approach for redundancy optimization problem

The present paper describes a simple heuristic approach to solve redundancy optimization problem without much of computation to help reliability designers choose an appropriate configuration. The solution technique is simpler than all previous methods and offers optimal results with a high success rate.     

Simplified algorithm for optimum availability allocation and redundancy optimization subject to cost constraint

A simplified algorithm is described for optimum availability allocation and redundancy optimization subject to cost constraint. A series reliability structure is considered and the problems of optimum availability allocation and redundancy optimization have been tackled separately. The algorithm basically involves the evaluation of selection factor d_i for the ith sybsystem. The fixed cost increment or the redundant unit is assigned to that subsystem for which d_i is a maximum.     

A bound heuristic algorithm for solving reliability redundancy optimization

This paper presents the concept of the bound region and proposes a solution method for the nonlinear integer programming refined from the optimal allocation of redundant components, which optimal solution has been proved in the bound region of the problem. In this method, any heuristic technique could be used to obtain a new and better bound point from any given bound point in the bound region. With some examples, the effect of the new algorithm and existing methods are compared. The results show that our method is better than any existing heuristic methods and can obtain optimal solutions in most cases.     

A new dynamic programming method for reliability & redundancy allocation in a parallel-series system

Reliability & redundancy allocation is one of the most frequently encountered problems in system design. This problem is subject to constraints related to the design, such as required structural, physical, and technical characteristics; and the components available in the market. This last constraint implies that system components, and their reliability, must belong to a finite set. For a parallel-series system, we show that the problem can be modeled as an integer linear program, and solved by a decomposition approach. The problem is decomposed into as many sub-problems as subsystems, one sub-problem for each subsystem. The sub-problem for a given subsystem consists of determining the number of components of each type in order to reach a given reliability target with a minimum cost. The global problem consists of determining the reliability target of subsystems. We show that the sub-problems are equivalent to one-dimensional knapsack problems which can be solved in pseudopolynomial time with a dynamic programming approach. We show that the global problem can also be solved by a dynamic programming technique. We also show that the obtained method YCC converges toward an optimal solution.     

Neural network optimization for redundancy allocation

System reliability optimization problems such as redundancy allocation are hard to solve exactly. Neural networks offer an alternative computational model for obtaining good approximate solutions for such problems. In this paper we present a neural network for solving the redundancy allocation problem for a n-stage parallel redundant system with separable objective function and constraints. The problem is formulated as a 0–1 integer programming problem and solved using the network. The performance of the network compare favourably with that of the best fit algorithm. The number of iterations taken by the network increases very slowly with increase in number of variables. Hence the network can easily solve large problems.     

A tabu search heuristic for bandwidth allocation in fixed WiMAX

In this paper, we propose a mathematical model with the aim to give guidelines for the dimensioning of fixed WiMAX bandwidth provisioning service to residential customers. Our proposal relies on the modeling of a CAC policy associated to a priority-based bandwidth sharing strategy. Both mechanisms aim to satisfy Quality of Service constraints while maximizing the gain of the system. But unlike in Khemiri et al. (Optimal call admission control for an IEEE 802.16 wireless metropolitan area network. In: International Conference on Network Control and Optimization, 2007, accepted), we do not necessarily seek here for an optimal CAC policy. We rather consider a coordinate convex CAC policy, whose mathematical modeling leads to a tractable analytical resolution. We chose the Guaranteed Minima (GM) policy, which consists in reserving a specified amount of bandwidth for each traffic class. The remaining bandwidth is shared between all traffic classes. The purpose of the paper is to determine, using an analytical model and a heuristic approach, the nearly-optimal sizes of the GM reservation thresholds associated to each type of connection. The main concern here is to propose simple and robust computational methods, which can be easily and efficiently used to dimension a realistic WiMAX network.     

协同通信系统中基于遍历容量的近似最优功率分配

传统半双工放大转发(AF)协同通信无论信道状态如何,总是采用协同通信模式,在一定程度上降低了系统自由度的利用率.基于此研究了"何时协同"与最优发送功率分配的问题.首先给出了AF系统的遍历容量表达式及其上界;然后以遍历容量上界为代价函数,提出一种适用于AF系统的近似最优功率分配方案(AOPA),通过比较源-目的端与中继-目的端的统计信道增益来判定是否需要采用协同通信模式;最后,还分析了在中继端按随机方向模型移动时AOPA方案的性能.理论分析和仿真结果表明,AOPA方案在系统容量和误码差错性能上均要优于等功率EPA方案.     

Optimal redundancy allocation for high availability routers

How to optimally allocate redundant routers for high availability (HA) networks is a crucial task. In this paper, a 5‐tuple availability function A (N, M, λ, µ, δ) is proposed to determine the minimum required number of standby routers to meet the desired availability (ρ) of an HA router, where N and M are the numbers of active routers and standby routers, respectively, and λ, µ, and δ are a single router's failure rate, repair rate, and failure detection and recovery rate, respectively. We have derived the availability function, and analytical results show that the failure detection and recovery rate (δ) is a key parameter for reducing the minimum required number of standby routers of an HA router. Thus, we also propose a High Availability Management (HAM) middleware, which was designed based on an open architecture specification, called OpenAIS, to achieve the goal of reducing takeover delay (1/δ) by stateful backup. We have implemented an HA Open Shortest Path First (HA‐OSPF) router, which consists of two active routers and one standby router, to illustrate the proposed HA router. Experimental results show that the takeover delays of the proposed HA‐OSPF router were reduced by 6, 37.3, and 98.6% compared with those of the industry standard approaches, the Cisco‐ASR 1000 series router, the Juniper MX series router, and the Virtual Router Redundancy Protocol (VRRP) router, respectively. In addition, in contract to the industry routers, the proposed HA router, which was designed based on an open architecture specification, is more cost‐effective, and its redundancy model can be more flexibly adjusted. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparison of two novel heuristic dynamic channel allocation techniques in cellular systems

Dynamic channel assignment (DCA) has been discussed in the literature as a way to achieve improved resource management in cellular networks. In the simplest dynamic allocation scheme, all channels are kept in a central pool and are used on a call-by-call basis. DCA is therefore a complex real time operation and various heuristic methods have been devised as mechanisms to give a fast and reliable solution to this problem. This paper examines the implementation of a DCA model using two approaches from the field of evolutionary computation. The first is the so called genetic algorithm (GA) and the second is the combinatorial evolution strategy (CES). Computer simulations evaluate and compare these proposed heuristic DCA schemes concerning their application to a cellular model for both uniform and non-uniform traffic load conditions. © 1998 John Wiley & Sons, Ltd.     

A new family of Grassmann space-time codes for non-coherent MIMO systems

A family of space-time codes suited for noncoherent multi-input multi-output (MIMO) systems is presented. These codes use all the complex degrees of freedom of the system, i.e. M/spl times/(1-(M/T)) symbols per channel use. They are constructed as codes on the Grassmann manifold G/sub T,M/(/spl Copf/) where T is the temporal codelength and M is the number of transmit antennas.     

Penalty function approach in heuristic algorithms for constrained redundancy reliability optimization

To solve the problem of constrained redundancy reliability optimization, several heuristic algorithms have been developed in the literature. Most of these algorithms search for the solutions remaining within the feasible boundary e.g. [15], [20]. Perhaps the most interesting & efficient heuristic algorithm in terms of solution quality is that given by KYA, in which the search is made not only in the feasible region but also into the bounded infeasible region by making an excursion, which returns to the feasible region with a possibly improved solution. In this paper, a heuristic algorithm based on the penalty function approach is proposed to solve the constrained redundancy optimization problem for complex systems. An excursion is made into the infeasible region, but an adaptive penalty function helps the search not to go too far into the infeasible region. Thus, promising feasible & infeasible regions of the search space are explored efficiently & effectively to identify finally an optimal or near optimal solution. Computational experiments are conducted on 11 sets of problems (10 with linear constraints, and 1 with nonlinear constraints); each with 10 different randomly generated initial solutions. Comparison is made between the proposed algorithm P-Alg, N-N algorithm [15], Shi algorithm [20], and KYA [9] . It is observed that P-Alg performs consistently better than others, showing an overall improvement in various measures of performance. Besides, as P-Alg does not require any assumptions on the nature of the objective & constraint functions, it can solve a wide variety of problems.     

A novel heuristic simulation‐optimization method for critical infrastructure in smart transportation systems

A simulation‐based optimization is a decision‐making tool that helps in identifying an optimal solution or a design for a system. An optimal solution and design are more meaningful if they enhance a smart system with sensing, computing, and monitoring capabilities with improved efficiency. In situations where testing the physical prototype is difficult, a computer‐based simulation and its optimization processes are helpful in providing low‐cost, speedy and lesser time‐ and resource‐consuming solutions. In this work, a comparative analysis of the proposed heuristic simulation‐optimization method for improving quality‐of‐service (QoS) is performed with generalized integrated optimization (a simulation approach based on genetic algorithms with evolutionary simulated annealing strategies having simplex search). In the proposed approach, feature‐based local (group) and global (network) formation processes are integrated with Internet of Things (IoT) based solutions for finding the optimum performance. Further, the simulated annealing method is applied for finding local and global optimum values supporting minimum traffic conditions. A small‐scale network of 50 to 100 nodes shows that genetic simulation optimization with multicriteria and multidimensional features performs better as compared to other simulation‐optimization approaches. Further, a minimum of 3.4% and a maximum of 16.2% improvement is observed in faster route identification for small‐scale IoT networks with simulation‐optimization constraints integrated model as compared to the traditional method. The proposed approach improves the critical infrastructure monitoring performance as compared to the generalized simulation‐optimization process in complex transportation scenarios with heavy traffic conditions. The communicational and computational‐cost complexities are least for the proposed approach.     

Variable-rate variable-power non-coherent M-FSK scheme for power limited systems

In response to the ongoing demand for low power applications, the two first authors have recently proposed in F.F. Digham and M.-S. Alouini (2004) a discrete-modulation and fixed-power non-coherent M-ary frequency shift keying scheme. In this paper, we propose an extension of that work by studying a discrete-modulation and continuous-power (DMCP) scheme for further power saving over Nakagami fading channels. The modulation level and power assignment are selected in order to minimize the average transmitted power while meeting average spectral efficiency and bit error rate constraints. We further investigate the problem with an additional peak power constraint. In this case, the modulation switching thresholds are shifted to higher values yielding reduction in both the achievable average spectral efficiency and the average transmitted power. However, the power loading function can be re-shaped to maintain the same average power of transmission for the cases on no-peak and with-peak power constraints. This reshaping is designed so as to involve constant power portions which can be of interest from a practical stand point.     

Exploring heuristic and optimum branching algorithms for image phylogeny

Currently, multimedia objects can be easily created, stored, (re)-transmitted, and edited for good or bad. In this sense, there has been an increasing interest in finding the structure of temporal evolution within a set of documents and how documents are related to one another overtime. This process, also known in the literature as Multimedia Phylogeny, aims at finding the phylogeny tree(s) that best explains the creation process of a set of near-duplicate documents (e.g., images/videos) and their ancestry relationships. Solutions to this problem have direct applications in forensics, security, copyright enforcement, news tracking services and other areas. In this paper, we explore one heuristic and one optimum branching algorithm for reconstructing the evolutionary tree associated with a set of image documents. This can be useful for aiding experts to track the source of child pornography image broadcasting or the chain of image distribution in time, for instance. We compare the algorithms with the state-of-the-art solution considering 350,000 test cases and discuss advantages and disadvantages of each one in a real scenario.     

A new heuristic method redundancy optimization in a consecutive-k-out-of-n:F system by fuzzy probability

In this paper it is presented heuristic method and its modification for solution of the redundancy optimization problem by fuzzy failure probabilities. This method is considered for consecutive-k-out-of-n:F system due to simplified expression for reliability importance of components. Method is based on the fuzzification and on the fuzziness importance of failure probabilities.     

Performance of non-coherent digital equalisers in DECT systems

The performance, in terms of bit error rate, of various data detectors at the output of a 1 bit differential demodulator for DECT receivers, is evaluated assuming static channels. The relevant result is that a one-tap decision feedback equaliser with an optimised threshold value yields a similar performance to the much more complex Viterbi algorithm. Moreover, in the presence of channels with a strong intersymbol interference, the decision feedback equaliser gains more than 2 dB in terms of SNR at BER 10³ with respect to the threshold detector     

On optimum power allocation for the V-BLAST

A unified analytical framework for optimum power allocation in the unordered V-BLAST algorithm and its comparative performance analysis are presented. Compact closed-form approximations for the optimum power allocation are derived, based on average total and block error rates. The choice of the criterion has little impact on the power allocation and, overall, the optimum strategy is to allocate more power to lower step transmitters and less to higher ones. High-SNR approximations for optimized average block and total error rates are given. The SNR gain of optimization is rigorously defined and studied using analytical tools, including lower and upper bounds, high and low SNR approximations. The gain is upper bounded by the number of transmit antennas, for any modulation format and type of fading channel. While the average optimization is less complex than the instantaneous one, its performance is almost as good at high SNR. A measure of robustness of the optimized algorithm is introduced and evaluated. The optimized algorithm is shown to be robust to perturbations in individual and total transmit powers. Based on the algorithm robustness, a pre-set power allocation is suggested as a low-complexity alternative to the other optimization strategies, which exhibits only a minor loss in performance over the practical SNR range.