Wireless Mesh Networks (WMNs) provide a flexible and low-cost technology to efficiently deliver broadband services to communities. In a WMN, a mesh router is deployed at each house, which acts both as a local access p...
详细信息
ISBN:
(纸本)9781538666142
Wireless Mesh Networks (WMNs) provide a flexible and low-cost technology to efficiently deliver broadband services to communities. In a WMN, a mesh router is deployed at each house, which acts both as a local access point and a relay to other nearby houses. Since mesh routers typically consist of off-the-shelf equipment, the major cost of the network is in the placement and management of Internet Transit Access Points (ITAP) which act as the connection to the internet. In designing a WMN, the aim is to minimize the number of ITAPs required whilst maximizing the traffic that could be served to each house. A multi-objective optimization algorithm is investigated to solve the WMN infrastructure placement problem, using crossover and mutation operators. A simulation based analysis is used to demonstrate the benefit of the proposed approach.
The fundamental goal of systems biology is to understand the dynamic aspects of cells and their behaviour. This organism is represented by a network so-called complex biomolecular network in which the nodes represent ...
详细信息
The fundamental goal of systems biology is to understand the dynamic aspects of cells and their behaviour. This organism is represented by a network so-called complex biomolecular network in which the nodes represent the different cellular components and the edges represent the interactions occurring among them. Through this network, it is easy to study the transition states and the dynamic behaviour of cells. Indeed, perturbing some nodes of the biomolecular network induce the transition of all the network. This process, known as the ”transittability”, expresses the idea of steering the complex biomolecular network from an unexpected state to a desired state. In this context, we are thus interested in how to use the transittability of biomolecular networks to increase the efficiency of translational medicine for improving human health and disease, including genetic and environmental factors of of patient’s well-being. This is a great opportunity to understand diseases, and find new diagnoses and treatments. Due to its complexity, the transittability of complex biomolecular networks can be considered as an optimization problem. Up to a recent date only few studies have been carried out in this problem. Most of them focused only on the minimization of the required nodes to steer the entire network, and others considered the minimization of the number of stimuli to be applied on the network. However, this assumption is not always realistic, because steering complex biomolecular networks is in general a multi-objectiveoptimization problem. It requires finding appropriate trade-offs among various objectives, for example between the appropriate nodes to be stimulated and the number of external stimuli to be used and their cost, and the impact on patient’s well-being. In this paper, the optimization of the transittability of complex biomolecular networks is investigated from the multi-objective perspective. In the mathematical model four criteria are considered simultane
Various intelligent algorithms have been applied to our daily lives,such as fuzzy theory,neural networks,and machine *** methods are widely used for solving many real-world problems;however,these algorithms also exhib...
详细信息
Various intelligent algorithms have been applied to our daily lives,such as fuzzy theory,neural networks,and machine *** methods are widely used for solving many real-world problems;however,these algorithms also exhibit deficiencies and *** paper introduces the recently improved algorithm,known as multi-objective particle swarm optimization,based on decomposition and dominance（D^2 MOPSO） in order to design the permanent magnet synchronous motor（PMSM） fuzzy controller for different *** means that the user can easily change the customized controller,according to their ***,this paper compares the final decision of the controller parameter with other algorithms:the multiobjective particle swarm optimization with crowding distance（MOPSO-CD）,and nondominated sorting genetic algorithm II（NSGA-II）.The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software ***,we implement the fuzzy controller in an embedded system（DSP28069） to demonstrate that our design matches the reality system response and meets the user’s demands with ease.
Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. H...
详细信息
Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objectiveoptimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.
The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due ...
详细信息
The optimal selection of a datacenter is one of the most important challenges in the structure of a network for the wide distribution of resources in the environment of a geographically distributed cloud. This is due to the variety of datacenters with different quality-of-service (QoS) attributes. The user's requests and the conditions of the service-level agreements (SLAs) should be considered in the selection of datacenters. In terms of the frequency of datacenters and the range of QoS attributes, the selection of the optimal datacenter is an NP-hard problem. A method is therefore required that can suggest the best datacenter, based on the user's request and SLAs. Various attributes are considered in the SLA;in the current research, the focus is on the four important attributes of cost, response time, availability, and reliability. In a geo-distributed cloud environment, the nearest datacenter should be suggested after receiving the user's request, and according to its conditions, SLA violations can be minimized. In the approach proposed here, datacenters are clustered according to these four important attributes, so that the user can access these quickly based on specific need. In addition, in this method, cost and response time are taken as negative criteria, while accessibility and reliability are taken as positive, and the multi-objective NSGA-II algorithm is used for the selection of the optimal datacenter according to these positive and negative attributes. In this paper, the proposed method, known as NSGAII_Cluster, is implemented with the Random, Greedy and MOPSO algorithms;the extent of SLA violation of each of the above-mentioned attributes are compared using four methods. The simulation results indicate that compared to the Random, Greedy and MOPSO methods, the proposed approach has fewer SLA violations in terms of the cost, response time, availability, and reliability of the selected datacenters.
A multi-objective approach to optimize wireless mesh network design with three conflicting objectives is presented: it minimizes the number of Internet Transit Access Points (ITAPs), maximizing the fairness of bandwid...
详细信息
ISBN:
(纸本)9781509042289
A multi-objective approach to optimize wireless mesh network design with three conflicting objectives is presented: it minimizes the number of Internet Transit Access Points (ITAPs), maximizing the fairness of bandwidth allocation and maximizing coverage to mesh clients. We discuss how such an approach can allow more effective use of an existing ITAP deployment, enabling a greater number of consumers to obtain Internet services. Previous contributions have formulated and solved this problem by using single-objective integer programming formulations. We instead apply the weighted-sum method and propose a heuristic algorithm with an efficient combination of move operators. This algorithm produces a set of effective optimization solutions under the ideal link network model.
MOEA/D is a well-known optimizationalgorithm in dealing with complex multi-objective problems. It employs a simple differential evolution strategy to generate offspring individuals. However, duo to the sensibility to...
详细信息
ISBN:
(纸本)9781538621653
MOEA/D is a well-known optimizationalgorithm in dealing with complex multi-objective problems. It employs a simple differential evolution strategy to generate offspring individuals. However, duo to the sensibility to the parameter setting in differential evolution strategy, MOEA/D performs poor in certain problems. To understand the influences of different DE strategies, this paper tries to investigate the overall performance of MOEA/D with different DE strategies. The experiment results demonstrate that DE/current-to-rand/1 strategy performs the best in all test problems.
Parameter optimization and calibration of the hydrological model has been one of the important research fields in hydrological forecasting. This paper is written to address the inherent defects that traditional parame...
详细信息
Parameter optimization and calibration of the hydrological model has been one of the important research fields in hydrological forecasting. This paper is written to address the inherent defects that traditional parameter optimization of Xinanjiang hydrological model with a single objective entails. These methods cannot fully exploit hydrological characteristics information from hydrological observation. We selected the Nash Sutcliffe coefficient, which is known to be biased for high flows and the logarithmic form of the Nash Sutcliffe coefficient that emphasize low-flow values as the objective functions. Then, we adopted the multi-objective optimization algorithms, such as the Nondominated Sorted Genetic algorithm-II (NSGAII) and the Third Evolution Step of Generalized Differential Evolution (GDE3), and the single-objectiveoptimizationalgorithm, Simulated Annealing (SA). These algorithms were applied in Heihe River Basin to calibrate parameters of the Xinanjiang hydrological model for long-term prediction of river discharges. Through the evaluation of the Pareto optimal parameter set derived from multi-objective optimization algorithms and the optimal solution obtained from the single objectivealgorithm, the results showed that the multi-objective optimization algorithms, in particular the NSGA-II algorithm, perform best to locate the Pareto optimal solutions in the parameter search space. They can also obtain better results with respect to the model parameters calibrated by the single objectivealgorithm. The major contribution of this work is the comparative application research of single-objectiveoptimization with the multi-objective optimization algorithms for the parameters optimization of the Xinanjiang model in the Heihe River basin.
In this paper a new and efficient hybrid multi-objective optimization algorithm is proposed for optimal placement and sizing of the Distributed generations (DGs) in radial distribution systems. A multiobjective Shuff...
详细信息
In this paper a new and efficient hybrid multi-objective optimization algorithm is proposed for optimal placement and sizing of the Distributed generations (DGs) in radial distribution systems. A multiobjective Shuffled Bat algorithm is proposed to evaluate the impact of DG placement and sizing for an optimal improvement of the distribution system with different load models. In this study, the ideal sizes and locations of DG units are found by considering the power losses, cost and voltage deviation as objective functions to minimize. Furthermore, the study is verified with voltage dependent load models like industrial, residential, commercial and mixed load models. The feasibility of the proposed technique is verified with the 33 bus distribution network and also the qualitative comparisons against a well-known technique, known as Non-dominated Sorting Genetic algorithm II (NSGA-II) is done and results are presented. (C) 2016 Elsevier Ltd. All rights reserved.
Mutation testing, which includes first order mutation (FOM) testing and higher order mutation (HOM) testing, appeared as a powerful and effective technique to evaluate the quality of test suites. The live mutants, whi...
详细信息
ISBN:
(纸本)9783662493816;9783662493809
Mutation testing, which includes first order mutation (FOM) testing and higher order mutation (HOM) testing, appeared as a powerful and effective technique to evaluate the quality of test suites. The live mutants, which cannot be killed by the given test suite, make up a significant part of generated mutants and may drive the development of new test cases. Generating live higher order mutants (HOMs) able to drive development of new test cases is considered in this paper. We apply multi-objective optimization algorithms based on our proposed objectives and fitness functions to generate higher order mutants using three strategies: HOMT1 (HOMs generated from all first order mutants), HOMT2 (HOMs generated from killed first order mutants) and HOMT3 (HOMs generated from not-easy-to-kill first order mutants). We then use mutation score indicator to evaluate, which of the three approaches is better suited to drive development of new test cases and, as a result, to improve the software quality.
暂无评论