In this article, we study the problem of join routing and scheduling of multi-team data flows multicasting in wireless multi-rate multi-hop networks with the objective of minimizing the time required to complete the t...
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ISBN:
(纸本)9781424441471
In this article, we study the problem of join routing and scheduling of multi-team data flows multicasting in wireless multi-rate multi-hop networks with the objective of minimizing the time required to complete the transmission transactions. We prove in this paper that the optimal join routing and scheduling problem is NP-hard. Instead of trying to solve the optimization problem, we reformulate the problem into an intuitive form which provides intuitions to achieve optimal solution. Base on these observations, we propose a heuristic algorithm that locally optimally chooses multicast relays and multicast rates. The aim is to locally minimize the schedule time length within two-hop range so that the total transmission time approaches optimal. This algorithm explores multi-team multicast advantages as well as the multi-rate range tradeoffs. Simulations based on practical ISM band channel model and current IEEE802.11 standards' parameters show superior of our proposed algorithm in approaching optimal solution.
This work concerns the application of a biologically inspired methodology for topology optimization to solve a practical vibration suppression problem. The method is based on L systems and its turtle interpretation as...
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A large number of real-world optimization problems requires making the decisions in the presence of uncertainty in some problem parameters. Such uncertainty or variations can complicate the process of decision making ...
A large number of real-world optimization problems requires making the decisions in the presence of uncertainty in some problem parameters. Such uncertainty or variations can complicate the process of decision making and change the optimality status of the obtained solution. Also, it is important to a decision maker to determine how sensitive and stable the optimal solution is with respect to different sources, magnitudes, and directions of variation. Post-optimality analysis can give a clear insight into these challenges. Due to the importance and the widespread application of optimization, interest in post-optimality analysis is increasing; however, to this point there is no dominant approach to perform post-optimality analysis that supplies the decision maker with easy-to-use information with reasonable computation effort especially for discrete problems. This study focuses on the practical implementation of the post-optimality analysis to several types of optimization problems. These optimization problems are continues linear programming (CLP), multi-objective integer linear programming (MOILP) and mixed integer linear problems (MILD). In general, the approaches jointly use sensitivity and stability relations to determine the effect of independent and simultaneous variations, in objective function coefficients or in the right-hand-side (RHS) of constraints, on the solution of a deterministic problem. For example, the proposed approach of CLP post-optimality analysis can compute stability limits (i.e. allowable variation ranges) within which the optimal basis remains unchanged. These limits can easily be adjusted based on the purpose of performing the post-optimality analysis as in the first case study of designing a stable steady-state target calculation of MPC. Moreover, the proposed approach of CLP stability analysis is more efficient than the current available approaches (e.g., tolerance approach). All the proposed approaches showed capability in handling the
When solving multi-objectiveoptimization problems subject to constraints in reliability-based design, it is desirable for the decision maker to have a sufficient number of solutions available for selection. However, ...
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When solving multi-objectiveoptimization problems subject to constraints in reliability-based design, it is desirable for the decision maker to have a sufficient number of solutions available for selection. However, many existing approaches either combine multiple objectives into a single objective or treat the objectives as penalties. This results in fewer optimal solutions than would be provided by a multi-objective approach. For such cases, a niched Pareto Genetic Algorithm (GA) may be a viable alternative. Unfortunately, it is often difficult to set penalty parameters that are required in these algorithms. In this paper, a multi-objectiveoptimization algorithm is proposed that combines a niched Pareto GA with a constraint handling method that does not need penalty parameters. The proposed algorithm is based on Pareto tournament and equivalence sharing, and involves the following components: search for feasible solutions, selection of non-dominated solutions and maintenance of diversified solutions. It deals with multiple objectives by incorporating the concept of Pareto dominance in its selection operator while applying a niching pressure to spread the population along the Pareto frontier. To demonstrate the performance of the proposed algorithm, a test problem is presented and the solution distributions in three different generations of the algorithm are illustrated. The optimal solutions obtained with the proposed algorithm for a practical reliability problem are compared with those obtained by a single-objectiveoptimization method, a multi-objective GA method, and a hybrid GA method.
This paper presents a selection and design scheme of multimodal command shapers using the multi-objective genetic algorithm (MOGA). A control scheme comprised of a command shaper and a collocated proportionalderivativ...
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This paper presents a selection and design scheme of multimodal command shapers using the multi-objective genetic algorithm (MOGA). A control scheme comprised of a command shaper and a collocated proportionalderivative control is employed in order to reduce end-point vibration without sacrificing the system's response speed. Command shaping causes a delay in the system's response and, also, reduces system vibration and, in this manner, the amount of vibration reduction and the response delay conflict with each other. Conventional methods can hardly provide a solution that satisfies several design objectives demanded by practical applications due to the competing nature of these objectives. Furthermore, the selection of a shaping technique is crucial since robustness and computational complexity depend on the shaping technique. This paper proposes a combined approach to selecting and designing command shapers using MOGA. A comparative assessment of the performance of the proposed approach with the conventional single-objective and weighted-sum genetic algorithm optimizationapproaches is also provided. The proposed technique can provide a wide range of solutions in a single run to conflicting design objectives and satisfy associated goals.
Springback for multi-curvature part is a very important factor influencing the quality of sheet metal forming. Accurate calculation and controlling of springback are essential for the design of tools for sheet metal *...
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Springback for multi-curvature part is a very important factor influencing the quality of sheet metal forming. Accurate calculation and controlling of springback are essential for the design of tools for sheet metal *** this paper,a springback quick compensation model is proposed to solve the problem of springback,which is based on fuzzy optimization improved GA-ANN algorithm and sheet metal forming springback experiment of multi-curvature part. The springback test results indicate that the springback compensation and analysis based on fuzzy optimization GA-ANN model are practical and *** calculation results with some precision can be *** can be taken as a reference for sheet metal forming tool design and controlling of springback.
In this article, we study the problem of join routing and scheduling of multi-team data flows multicasting in wireless multi-rate multi-hop networks with the objective of minimizing the time required to complete the t...
详细信息
In this article, we study the problem of join routing and scheduling of multi-team data flows multicasting in wireless multi-rate multi-hop networks with the objective of minimizing the time required to complete the transmission transactions. We prove in this paper that the optimal join routing and scheduling problem is NP-hard. Instead of trying to solve the optimization problem, we reformulate the problem into an intuitive form which provides intuitions to achieve optimal solution. Base on these observations, we propose a heuristic algorithm that locally optimally chooses multicast relays and multicast rates. The aim is to locally minimize the schedule time length within two-hop range so that the total transmission time approaches optimal. This algorithm explores multi-team multicast advantages as well as the multi-rate range tradeoffs. Simulations based on practical ISM band channel model and current IEEE802.11 standards' parameters show superior of our proposed algorithm in approaching optimal solution.
Conventional economic load dispatch problem uses deterministic models, which are however not able to reflect some real situations in practical applications since certain inaccurate and uncertain factors are normally i...
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Conventional economic load dispatch problem uses deterministic models, which are however not able to reflect some real situations in practical applications since certain inaccurate and uncertain factors are normally involved in system operations. Stochastic models are more suited to be used for investigating some of the power dispatch problems. In this paper, both deterministic and stochastic models are first formulated, and then an improved particle swarm optimization (PSO) method is developed to deal with the economic load dispatch while Simultaneously considering the environmental impact. Comparative studies are carried out to examine the effectiveness of the proposed approach. First, a comparison is made between the proposed PSO approach and other approaches including weighted aggregation and evolutionary optimization. Then, based on the proposed PSO, the impacts of different problem formulations including stochastic and deterministic models on power dispatch results are investigated and analyzed. (C) 2008 Elsevier B.V. All rights reserved.
This paper presents a new synthesis method for designing complex fiber Bragg gratings (FBGs). The method is based on a multi-objective Lagrange-multiplier-constrained optimization (LMCO), to which various constraints ...
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This paper presents a new synthesis method for designing complex fiber Bragg gratings (FBGs). The method is based on a multi-objective Lagrange-multiplier-constrained optimization (LMCO), to which various constraints on the designed filters can be added in consideration of practical application demands and fabrication requirements. The maximum amplitude of the index modulation profiles of the designed FBGs can be substantially reduced under constrained conditions. In contrast with the layer-peeling (LP) algorithm, the LMCO method can easily incorporate different types of requirements in terms of a user-defined cost function. Compared to stochastic approaches such as genetic algorithms, the proposed method is likewise a direct optimization method, but without using random numbers, and therefore has a smoother coupling coefficient profile as well as faster convergence. A theoretical model and investigation have been made in this study. A narrowband dispersionless FBG filter for optical fiber communication was designed, and its simulation results were compared with those of the LP algorithm. The study results demonstrate that the LMCO algorithm can provide an alternative for practical and complex fiber grating filters. (C) 2008 Society of Photo-Optical Instrumentation Engineers.
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