This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied
This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied
作者：
Roy, ADas, SKUniv Texas
Dept Comp Sci & Engn Ctr Res Wireless Mobil & Networking Arlington TX 76019 USA
With the increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS) based routing offers significant challenges. multimedia applications, such as video conferencing or real...
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With the increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS) based routing offers significant challenges. multimedia applications, such as video conferencing or real-time streaming of stock quotes, require strict QoS guarantee on bandwidth and delay parameters while communicating among multiple hosts. These applications give rise to the need for efficient multicast routing protocols, which will be able to determine multicast routes that satisfy different QoS constraints simultaneously. However, designing such protocols for optimizing multiple objectives, is computationally intractable. Precisely, discovering optimal multicast routes is an NP-hard problem when the network state information is inaccurate - a common scenario in wireless networks. Based on the multi-objectivegenetic algorithm (MOGA), in this paper we propose a QoS-based mobile multicast routing protocol (QM(2)RP) that determines near-optimal routes on demand. Our protocol attempts to optimize multiple QoS parameters, namely end-to-end delay, bandwidth requirements, and residual bandwidth utilization. Furthermore, it is fast and efficient in tackling dynamic multicast group membership information arising due to user mobility in wireless cellular networks. Simulation results demonstrate that the proposed protocol is capable of discovering a set of QoS-based, near-optimal multicast routes within a few iterations, even with imprecise network information. Among these routes one can choose the best possible one depending on the specified QoS requirements. The protocol is also scalable and yields lower multicast call-blocking rates for dynamic multicast group size in large networks.
作者：
Roy, ADas, SKUniv Texas
Dept Comp Sci & Engn Ctr Res Wireless Mobil & Networking Arlington TX 76019 USA
With the increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS) based routing offers significant challenges. multimedia applications, such as video conferencing or real...
详细信息
With the increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS) based routing offers significant challenges. multimedia applications, such as video conferencing or real-time streaming of stock quotes, require strict QoS guarantee on bandwidth and delay parameters while communicating among multiple hosts. These applications give rise to the need for efficient multicast routing protocols, which will be able to determine multicast routes that satisfy different QoS constraints simultaneously. However, designing such protocols for optimizing multiple objectives, is computationally intractable. Precisely, discovering optimal multicast routes is an NP-hard problem when the network state information is inaccurate - a common scenario in wireless networks. Based on the multi-objectivegenetic algorithm (MOGA), in this paper we propose a QoS-based mobile multicast routing protocol (QM(2)RP) that determines near-optimal routes on demand. Our protocol attempts to optimize multiple QoS parameters, namely end-to-end delay, bandwidth requirements, and residual bandwidth utilization. Furthermore, it is fast and efficient in tackling dynamic multicast group membership information arising due to user mobility in wireless cellular networks. Simulation results demonstrate that the proposed protocol is capable of discovering a set of QoS-based, near-optimal multicast routes within a few iterations, even with imprecise network information. Among these routes one can choose the best possible one depending on the specified QoS requirements. The protocol is also scalable and yields lower multicast call-blocking rates for dynamic multicast group size in large networks.
The timetabling problem is generally large, highly constrained and discrete in nature. This makes solution by exact optimisation methods difficult. Therefore, often a heuristic search is deemed acceptable providing a ...
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The timetabling problem is generally large, highly constrained and discrete in nature. This makes solution by exact optimisation methods difficult. Therefore, often a heuristic search is deemed acceptable providing a simple (non-optimal) solution. This paper discusses the timetabling problem for a university department, where a large-scale integer goal programming (IGP) formulation is implemented for its efficient optimal solution in two phases. The first phase allocates lectures to rooms and the second allocates start-times to lectures. Owing to the size and complicated nature of the model, an initial analysis procedure is employed to manipulate the data to produce a more manageable model, resulting in considerable reductions in problem size and increase of performance. Both phases are modelled as IGPs. Phase 1 is solved using a state-of-the-art IGP optimisation package. However, due to the scale of the model, phase 2 is solved to optimality using a genetic algorithm approach.
In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movemen...
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In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes. The "urgency degree" term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objectivegenetic algorithm (MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.
The widespread use of high power semiconductors has given rise to a host of thermal design issues. The integration of various power semiconductor devices into a single programmable package as envisioned by the Navy...
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ISBN:
(纸本)0780366492
The widespread use of high power semiconductors has given rise to a host of thermal design issues. The integration of various power semiconductor devices into a single programmable package as envisioned by the Navy's Power Electronic Building Block (PEBB) program is projected to significantly increase device heat dissipation rates up to 2-3W/mm(2). Traditional cooling techniques, including natural or forced convection air-cooling, turn out to be inadequate at such high power levels. Liquid cooled heat sinks or cold plates are increasingly being employed for such modules. Along with the development of novel thermal management techniques, there is also a growing interest in thermal design methodologies. One of the key issues facing the packaging designer is the selection of an appropriate cold plate and the optimal placement of components on it. Traditionally, optimal component placement studies have focused on single objective optimization [1,2]. Osterman et al. [3] developed a force directed placement methodology. Humphrey et al. [4] first introduced adaptive search procedures, such as geneticalgorithms, to study component placement on printed wiring boards. All studies involved modeling the device as a discrete heat source and placed in certain specified locations. The architecture of the device was not considered, particularly in the context of multi-objective optimization. This study investigates the multi-objective placement optiization of power electronic components on liquid cooled heat sinks. The placement methodology described in this paper is shown in Fig (1)*. The two main components involved are an optimization algorithm and a heat transfer solver. A multiobjectivegenetic algorithm (MOGA) [5] is chosen as the optimizer. The actual heat transfer in the system is usually complicated due to the presence of multiple materials and coupled thermal paths and may require time intensive three-dimensional heat transfer solvers. These solvers are inefficient and
Process planning and scheduling are actually interrelated and should be solved simultaneously. Most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when co...
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Process planning and scheduling are actually interrelated and should be solved simultaneously. Most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. The initial part of this paper describes a genetic algorithm (GA) based algorithm that only considers the time aspect of the alternative machines. The scope of consideration is then further extended to include the processing capabilities of alternative machines, with different tolerance limits and processing costs. In the proposed method based on GAs, the processing capabilities of the machines, including processing costs as well as number of rejects produced in alternative machine are considered simultaneously with the scheduling of jobs. The formulation is based on multi-objective weighted-sums optimization, which are to minimize makespan, to minimize total rejects produced and to minimize the total cost of production. A comparison is done with the traditional sequential method and the multi-objectivegenetic algorithm (MOGA) approach, based on the Pareto optimal concept.
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