An efficient mesh router nodes placement in wireless mesh networks based on moth-flame optimization algorithm

One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi-objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.     

Hybrid genetic-paired-permutation algorithm for improved VLSI placement

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N²).     

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Cloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state‐of‐the‐art multiobjective VM placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.     

An efficient heuristic for standard-cell placement

A new method is presented for optimization of the one and two-dimensional quadratic assignment problem. The method is suitable for placement problems as they appear in sea-of-gates and standard-cell layout styles for VLSI design. The method is based on recursive partitioning and is a generalization of the method introduced by Kuh et al. It is more flexible than Kuh's method because it does not require a special distribution of the external connections on the boundary of the chip. The time complexity of the algorithm is O(_n Iog²_n). A numerical evaluation of the method is presented, which shows its efficiency for generating near optimal solutions for the quadratic assignment problem as well as for practical standard-cell placement problems.     

SaaS多租户数据放置的优化调整策略研究

采用副本技术的SaaS多租户数据为满足租户访问及服务提供商管理的需求,在云中多节点必须合理放置。针对多租户数据特征和节点负载情况,对负载超重节点或超轻节点,通过调整数据副本数目和位置进行放置策略维护及优化,在满足所有租户SLA需求的同时最小化服务提供商的成本代价,实现服务质量与管理成本2个目标的均衡。并通过实验与随机放置策略和贪婪放置策略进行比较,证明该策略的可行性和有效性。     

Aggregate node placement for maximizing network lifetime in sensor networks

Sensor networks have been receiving significant attention due to their potential applications in environmental monitoring and surveillance domains. In this paper, we consider the design issue of sensor networks by placing a few powerful aggregate nodes into a dense sensor network such that the network lifetime is significantly prolonged when performing data gathering. Specifically, given K aggregate nodes and a dense sensor network consisting of n sensors with K ≪ n, the problem is to place the K aggregate nodes into the network such that the lifetime of the resulting network is maximized, subject to the distortion constraints that both the maximum transmission range of an aggregate node and the maximum transmission delay between an aggregate node and its covered sensor are met. This problem is a joint optimization problem of aggregate node placement and the communication structure, which is NP‐hard. In this paper, we first give a non‐linear programming solution for it. We then devise a novel heuristic algorithm. We finally conduct experiments by simulation to evaluate the performance of the proposed algorithm in terms of network lifetime. The experimental results show that the proposed algorithm outperforms a commonly used uniform placement schema — equal distance placement schema significantly. Copyright © 2010 John Wiley & Sons, Ltd.     

An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

In this paper, we consider the problem of maximizing the throughput of remote sensing satellite networks. In such networks, the link capacities and routing matrices are varying over time. We propose a convex optimization‐based analytical framework for the problem. To maximize the network throughput under the premise of satisfying the delay constraint, we formulate the data transmission schedule into an optimization problem aiming at maximizing the delay‐constrained throughput. Considering the fact that the future link capacities cannot be accurately known in the actual situation, we propose a heuristic and distributed framework on the basis of model predictive control for approximately solving the problem. This framework can be used to design remote sensing data transmission schedules under various scenarios. We adopt a generic example to simulate and analyze the framework. The simulation results show that the proposed analytic framework can obtain the approximate solution that is very close to the optimal solution by solving the convex optimization problem step‐by‐step. The heuristic algorithm based on model predictive control can obtain the approximate solution, which is very close to the optimal solution in distributed scenario.     

An optimization model for placement of wavelength converters to minimize blocking probability in WDM networks

The introduction of wavelength converters in wavelength division multiplexing (WDM) networks can reduce the blocking probabilities of calls. In this paper, we study the problem of placing a given number of converters in a general topology WDM network such that the overall system blocking probability is minimized. The original contributions of this work are the following: 1) formulation of success probability in a network as a polynomial function of the locations of converters; 2) proposal of an optimization model of the converter placement problem as the minimization of a polynomial function of 0-1 variables under a linear constraint, so that standard optimization tools can be employed to solve the problem; and 3) design of a search algorithm that can efficiently find the optimal solution to the converter placement problem. Experiments have been conducted to demonstrate the effectiveness of the proposed model and the efficiency of the algorithm.     

An efficient communication relay placement algorithm for content‐centric wireless mesh networks

In this paper, we investigate a communication relay placement problem to optimize the network throughput in a content‐centric wireless mesh networks (WMN), in which the WMN is enhanced by including a small set of communication relays and a subset of wireless mesh routers serving as storage nodes. Specifically, we first define the communication relay placement problem in content‐centric WMNs. We then model the problem as a mathematical programming and propose a linear programming approach for calculating the achievable network throughput when the positions of communication relays are fixed. Next, to optimally placing the communication relays, we formulate an integer linear programming problem and we develop an efficient near‐optimal approximation algorithm based on linear programming relaxation. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

An adaptive energy‐efficient framework for time‐constrained optical backbone networks

Power management has emerged as a challenge of paramount importance having strong social and financial impact in the community. The rapid growth of information and communication technologies made backbone networks a serious energy consumer. Concurrently, backbone networking is deemed as one of the most promising areas to apply energy efficient frameworks. One of the most popular energy efficient techniques, in the context of backbone networks, is to intentionally switch off nodes and links that are monitored underutilized. Having in mind that optical technology has thoroughly dominated modern backbone networks, the function of switching off techniques entails fast operation and rigorous decision‐making because of the tremendous speed of the underlying optical media. This paper addresses this challenge by introducing a novel, adaptive, and efficient power management scheme for large‐scale backbone networks. The proposed framework exploits traffic patterns and dynamics in order to effectively switch off the set of network entities in a periodic fashion. An adaptive decision‐making algorithm is presented to maximize the network energy gains with respect to time constraints as well as QoS guarantees. The conducted simulation results reveal considerable improvements when applying the proposed framework compared with other inflexible energy efficient schemes. Copyright © 2015 John Wiley & Sons, Ltd.     

APWL-Y: An accurate and efficient wirelength estimation technique for hexagon/triangle placement

The Y architecture has recently received much attention due to its many potential advantages, such as substantially reduced wirelength, power consumption and significantly improved throughput. To fully utilize the virtues of Y architecture, several hexagon/triangle placement (HTP) algorithms suitable for the Y architecture were presented, however the wirelength optimization is not included in the algorithms. Wirelength estimation is fundamental to guide the wirelength optimization process in early design stages. In this paper, we present an accurate and efficient wirelength estimation technique called APWL-Y appropriate for the Y architecture, and especially for HTP floorplanner and placer. The average error of APWL-Y is 4.41% for 1.57 million nets from industrial circuits. When developing APWL-Y, we find out that 3-SMT wirelength is a power function of aspect ratio of bounding box of the given n-pin nets. The time complexity of APWL-Y is O(n). APWL-Y is very effective to guide the wirelength optimization in a HTP placer. Moreover, we develop an efficient HTP algorithm with wirelength optimization driven by APWL-Y estimator. The placement results by our placer subject to different optimization objectives are presented. Compared to the HTP placer with only area optimization, our placer can reduce the wirelength by 54.3% with a small area overhead of 9.07% on average. In addition, we explore the HPWL technique in the Y architecture. To the best of our knowledge, this paper is the first in-depth study on wirelength estimation technique in Y architecture and HTP floorplanning optimization with consideration of interconnects.     

An analog/digital pre-distorter using particle swarm optimization for RF power amplifiers

In this paper, a novel pre-distorter is presented using the particle swarm optimization (PSO) for an RF power amplifier linearization has been presented. We used the PSO in order to design of an efficient pre-distorter for the linearization of the output of an RF power amplifier by using the output data of the proposed power amplifier. The PSO is implemented to estimate and optimize the coefficient parameters of the work function in the proposed pre-distorter block diagram. The proposed method using PSO is most efficient because this approach is independent of the output of the power amplifier. The proposed method has been simulated with two-tone input signal and output power spectrum has been compared, where the obtained adjacent channel leakage ration (ACLR) is better than 50 dBc for both channels. Therefore, a quite significant improvement in linearity is achieved.     

An optimal spectrum‐balancing algorithm for digital subscriber lines based on particle swarm optimization

This paper presents a new algorithm for optimal spectrum balancing in modern digital subscriber line (DSL) systems using particle swarm optimization (PSO). In DSL, crosstalk is one of the major performance bottlenecks, therefore various dynamic spectrum management algorithms have been proposed to reduce excess crosstalks among users by dynamically optimizing transmission power spectra. In fact, the objective function in the spectrum optimization problem is always nonconcave. PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm optimizes the weighted rate sum. These weights allow the system operator to place differing qualities of service or importance levels on each user, which makes it possible for the system to avoid the selfish‐optimum. We can show that the proposed algorithm converges to the global optimal solutions. Simulation results demonstrate that our algorithm can guarantee fast convergence within a few iterations and solve the nonconvex optimization problems efficiently. Copyright © 2008 John Wiley & Sons, Ltd.     

An improved distance‐based ant colony optimization routing for vehicular ad hoc networks

Vehicular ad hoc network (VANET) has earned tremendous attraction in the recent period due to its usage in a wireless intelligent transportation system. VANET is a unique form of mobile ad hoc network (MANET). Routing issues such as high mobility of nodes, frequent path breaks, the blind broadcasting of messages, and bandwidth constraints in VANET increase communication cost, frequent path failure, and overhead and decrease efficiency in routing, and shortest path in routing provides solutions to overcome all these problems. Finding the shortest path between source and destination in the VANET road scenario is a challenging task. Long path increases network overhead, communication cost, and frequent path failure and decreases routing efficiency. To increase efficiency in routing a novel, improved distance‐based ant colony optimization routing (IDBACOR) is proposed. The proposed IDBACOR determines intervehicular distance, and it is triggered by modified ant colony optimization (modified ACO). The modified ACO method is a metaheuristic approach, motivated by the natural behavior of ants. The simulation result indicates that the overall performance of our proposed scheme is better than ant colony optimization (ACO), opposition‐based ant colony optimization (OACO), and greedy routing with ant colony optimization (GRACO) in terms of throughput, average communication cost, average propagation delay, average routing overhead, and average packet delivery ratio.     

An optimized small-cell planning procedure for Heterogeneous Network to improve network energy efficiency

Cell planning in conventional networks has gained more attention as it directly affects the network performance and deployment cost. Existing cell planning methodologies are framed either with identical base stations or constructing a network without any infrastructure. But heterogeneous networks (HetNets) allow the service provider to deploy small cells over the region to enhance the network performance and signal coverage probability. Thus, a small-cell planning procedure is presented in this research work considering the low-powered base station and deployment cost to enhance the energy efficiency of the HetNet. An adaptive fuzzy expert system is used for cell dimensioning, and a nature-inspired ant colony optimization model is employed for automatic base station placement. Simulation analysis demonstrates that the proposed small-cell planning procedure attains better energy efficiency and user satisfaction ratio compared to conventional planning strategies. In the two cases of simulation analysis, the proposed model attains an average of 85% user satisfaction ratio for case 1 and 87% for case 2, which is better than existing strategies like density-based spatial clustering of applications with noise (DBSCAN), k-means, and number-based spatial clustering (NBSC) algorithms.