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.     

An improved method for a redundancy optimization problem

The present note suggests an improvement over an earlier method for a redundancy optimization problem. There is a drastic reduction in the number of steps required to arrive at the optimal allocation. A numerical example illustrates the technique.     

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     

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.     

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.     

An efficient algorithm for spare allocation problems

The spare allocation problem in redundant RAM is to replace faulty rows/columns of memory cells with spare rows/columns. To solve the problem, comparison-based search tree structures were used in traditional exact algorithms. These algorithms are not efficient for large problems because significant amounts of data have to be retained and copied in order to generate new partial solutions. Many data may need to be compared for the removal of each redundant partial solution. To overcome these drawbacks, an efficient algorithm is proposed in this paper. The algorithm transforms a spare allocation problem into Boolean functions, and the renowned BDD is used to manipulate them. Experimental results indicate that the proposed algorithm is very efficient in terms of speed and memory requirements. It may also be useful for problems which can be modeled as constraint bipartite vertex cover problems.     

An energy efficient stable clustering approach using fuzzy extended grey wolf optimization algorithm for WSNs

Wireless Networks - Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a...     

Modeling and efficient optimization for object-based scalabilityand some related problems

MPEG-4 is the first visual coding standard that allows coding of scenes as a collection of individual audio-visual objects. We present mathematical formulations for modeling object-based scalability and some functionalities that it brings with it. Our goal is to study algorithms that aid in semi-automating the authoring and subsequent selective addition/dropping of objects from a scene to provide content scalability. We start with a simplistic model for object-based scalability using the "knapsack problem"-a problem for which the optimal object set can be found using known schemes such as dynamic programming, the branch and bound method and approximation algorithms. The above formulation is then generalized to model authoring or multiplexing of scalable objects (e.g., objects encoded at various target bit-rates) using the "multiple choice knapsack problem." We relate this model to several problems that arise in video coding, the most prominent of these being the bit allocation problem. Unlike previous approaches to solve the operational bit allocation problem using Lagrangean relaxation, we discuss an algorithm that solves linear programming (LP) relaxation of this problem. We show that for this problem the duality gap for Lagrange and LP relaxations is exactly the same. The LP relaxation is solved using strong duality with dual descent-a procedure that can be completed in "linear" time. We show that there can be at most two fractional variables in the optimal primal solution and therefore this relaxation can be justified for many practical applications. This work reduces problem complexity, guarantees similar performance, is slightly more generic, and provides an alternate LP-duality based proof for earlier work by Shoham and Gersho (1988). In addition, we show how additional constraints may be added to impose inter-dependencies among objects in a presentation and discuss how object aggregation can be exploited in reducing problem complexity. The marginal analysis approach of Fox (1966) is suggested as a method of re-allocation with incremental inputs. It helps in efficiently re-optimizing the allocation when a system has user interactivity, appearing or disappearing objects, time driven events, etc. Finally, we suggest that approximation algorithms for the multiple choice knapsack problem, which can be used to quantify complexity vs. quality tradeoff at the encoder in a tunable and universal way.     

An efficient numerical approach for modeling microstrip-typeantennas

An efficient numerical approach to model antennas that include a microstrip element radiating in the presence of material layers is developed. The class of antennas considered is fed through the ground plane by a coaxial transmission line. The reaction integral equation is formulated by treating the coaxial aperture as part of the antenna. The substrate thickness can be arbitrary, making this numerical technique suitable for high-frequency applications. The effects of the substrate are also included in the analysis. Numerical results are obtained for the current distribution and input impedance. The algorithm is validated with experimental results     

An ant colony optimization algorithm for the redundancy allocation problem (RAP)

This paper uses an ant colony meta-heuristic optimization method to solve the redundancy allocation problem (RAP). The RAP is a well known NP-hard problem which has been the subject of much prior work, generally in a restricted form where each subsystem must consist of identical components in parallel to make computations tractable. Meta-heuristic methods overcome this limitation, and offer a practical way to solve large instances of the relaxed RAP where different components can be placed in parallel. The ant colony method has not yet been used in reliability design, yet it is a method that is expressly designed for combinatorial problems with a neighborhood structure, as in the case of the RAP. An ant colony optimization algorithm for the RAP is devised & tested on a well-known suite of problems from the literature. It is shown that the ant colony method performs with little variability over problem instance or random number seed. It is competitive with the best-known heuristics for redundancy allocation.     

An efficient PASTd-algorithm implementation for multiple directionof arrival tracking

We propose a simple direction-of-arrival (DOA) tracking scheme involving the projection approximation subspace tracking with deflation (PASTd)-algorithm and a Kalman filter. The DOAs are obtained via a Newton-type method initialized with the DOAs predicted by the Kalman filter. The data association is then avoided. The tracking capability of the proposed algorithm is demonstrated by computer simulations in scenarios involving targets with crossing trajectories     

A fast algorithm for a redundancy optimization problem

This paper presents a simple heuristic technique to arrive at the optimal redundancy allocation. There is a drastic reduction in the number of steps because of the criterion evolved, which allows for the addition of one or more units, simultaneously, at more than one stage. Examples have been provided to demonstrate the effectiveness of the technique.     

An optimization technique for efficient channel allocation in cellular network

For today’s wireless mobile communication systems, efficient use of limited radio spectrum with minimum interferences is required. Itinvestigates an Optimal Genetic Algorithm approach (GA) for Hybrid Channel allocation (NP hard) focusing on reduction in interference in cellular Network. Obtained an interference graph based fitness function to enhance the performance of HCA for interference reduction. It is shown that the use of integer genetic representation for Crossover and mutation operation enhances the speed of GA leading to less computation time. Comparison of proposed method is done with reported literature for KUNZ 4 which results in less co-channel and co-site interference depicted by interfering edges and also number of generations required are less. The result for KUNZ 1, KUNZ 2 and KUNZ 3 are obtained with minimum interference along with computation time.     

An improved random walk approach for yield optimization

In this paper, the traditional random walk approach for yield optimization is modified to further improve its efficiency. The orthogonal array of experiment design is employed as an alternative to generating sample points in the circuit parameter space. In addition, the step of the random walk is modified to be adapt. Finally, an example is given to show its high efficiency.     

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.     

Dynamic redundancy allocation using Monte-Carlo optimization

Monte-carlo optimization (Random search) technique is used for the redundancy allocation problem for maximizing the system reliability subject to cost constraint. The computational time is quite short. The algorithm is clearly described and one example is shown.     

Branch-and-bound redundancy optimization for a series system withmultiple-choice constraints

This paper considers a redundancy optimization problem in which multiple-choice and resource constraints are incorporated. The problem is expressed as a nonlinear integer programming problem and is characterized as an NP-hard problem. The purpose of the paper is to develop a SSRP (solution space reduction procedure). Therefore, the problem is analyzed first to characterize some solution properties. An iterative SSRP is then derived using those solution properties. Finally, the iterative SSRP is used to define an efficient B&BP (branch-and-bound procedure) algorithm. Experimental tests show how dramatically the SSRP can work on removing any intermediately-found unnecessary decision variables from further consideration in solution search, and how efficient this B&BP is     

A bound dynamic programming for solving reliability redundancy optimization

This paper presents a bound dynamic programming for solving reliability optimization problems, in which the optimal solution is obtained in the bound region of the problem by using dynamic programming. This algorithm is based on the studies of the characters of the problem and Misra [IEEE Trans. Reliability 40, 81–91 (1991)] bound search technique. With some examples, the proposed algorithm has been found to be more economical and effective than Misra integer programming to obtain the exact solutions of reliability redundancy optimization problems.     

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.     

An efficient fault-tolerant approach for Mobile IP in wireless systems

This paper presents the fault tolerance of Mobile IP in wireless systems. Mobile IP can support wireless users with continuous network connections while changing locations. It is achieved by allocating a number of mobility agents (foreign agents and home agents) in the architecture of a wireless system. If a failure occurs in a mobility agent, the wireless users located in the coverage area of the faulty mobility agent will lose their network connections. To tolerate the failures of mobility agents, this paper proposes an efficient approach to maintaining the network connections of wireless users without being affected by the failures. Once detecting a failure in a mobility agent, failure-free mobility agents are dynamically selected to be organized as a backup set to take over the faulty mobility agent. Compared to the previous approaches, the proposed approach does not take any actions against failures during the failure-free period. Besides, the hardware redundancy technique is also not used in the proposed approach. The overhead of the proposed approach is analyzed using the M/G/c/c queuing model. The results show that the proposed approach can effectively resolve the fault-tolerant problem of Mobile IP in wireless systems.