7426259
Modelling the Population Distribution in Multi-objective Optimization by Generative Topographic Mapping
Under mild conditions, the Pareto set of a continuous multi-objective optimization problem exhibits certain regularity. We have recently advocated taking into consideration such regularity in designing multi-objective evolutionary algorithms. Following our previous work on using Local Principal Component Analysis for capturing the regularity, this paper presents a new approach for acquiring and using the regularity of the Pareto set in evolutionary algorithms. The approach is based on the Generative Topographic Mapping and can be regarded as an Estimation of Distribution Algorithm. It builds models of the distribution of promising solutions based on regularity patterns extracted from the previous search, and samples new solutions from the models thus built. The proposed algorithm has been compared with two other state-of-the-art algorithms, NSGA-II and SPEA2 on a set of test problems.
16216432
A Multiobjective Differential Evolution Based on Decomposition for Multiobjective Optimization with Variable Linkages
Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblems by a conventional decomposition method. The optimal solution to each of these problems is a Pareto optimal solution to the MOP under consideration. An appropriate decomposition could make these individual Pareto solutions evenly distribute along the Pareto optimal front. MOEA/D aims at solving these scalar optimization subproblems simultaneously. In this paper, we propose, under the framework of MOEA/D, a multiobjective differential evolution based decomposition (MODE/D) for tackling variable linkages. Our experimental results show that MODE/D outperforms several other MOEAs on several test problems with variable linkages.
29533358
On the discrete-time dynamics of a PCA learning algorithm
Abstract We propose a principal component analysis algorithm based on our previous work and analyze its deterministic discrete-time (DDT) system. We prove that its DDT system is always bounded for the small constant learning gain and can almost always deliver a principal component. The difference between Oja's algorithm and our proposed algorithm cannot be observed from their corresponding deterministic continuous-time systems.
1741788
A Compound TCP Approach for High-Speed and Long Distance Networks
Many applications require fast data transfer over high speed and long distance networks. However, standard TCP fails to fully utilize the network capacity due to the limitation in its conservative congestion control (CC) algorithm. Some works have been proposed to improve the connection 鈂s throughput by adopt- ing more aggressive loss-based CC algorithms. These algorithms, although can effectively improve the link utilization, have the weakness of poor RTT fairness. Further, they may severely de- crease the performance of regular TCP flows that traverse the same network path. On the other hand, pure delay-based ap- proaches that improve the throughput in high-speed networks may not work well when the traffic is mixed with both delay- based and greedy loss-based flows. In this paper, we propose a novel Compound TCP (CTCP) approach, which is a synergy of delay-based and loss-based approach. Specifically, we add a scal- able delay-based component into the standard TCP Reno conges- tion avoidance algorithm (a.k.a., the loss-based component). The sending rate of CTCP is controlled by both components. This new delay-based component can rapidly increase sending rate when network path is under utilized, but gracefully retreat in a busy network when bottleneck queue is built. Augmented with this delay-based component, CTCP provides very good bandwidth scalability with improved RTT fairness, and at the same time achieves good TCP-fairness, irrelevant to the windows size. We developed an analytical model of CTCP and implemented it on the Windows operating system. Our analysis and experiment results verify the properties of CTCP.
11623385
Resource allocation for multimedia streaming over the Internet
This paper addresses the resource allocation problem for multiple media streaming over the Internet. First, we present an end-to-end transport architecture for multimedia streaming over the Internet. Second, we propose a new multimedia streaming TCP-friendly protocol (MSTFP), which combines forward estimation of network conditions with information feedback control to optimally track the network conditions. Third, we propose a novel resource allocation scheme to adapt media rate to the estimated network bandwidth using each media's rate-distortion function under various network conditions. By dynamically allocating resources according to network status and media characteristics, we improve the end-to-end quality of services (QoS). Simulation results demonstrate the effectiveness of our proposed schemes.
2460253
Approximate processing of massive continuous quantile queries over high-speed data streams
Quantile computation has many applications including data mining and financial data analysis. It has been shown that an /spl epsi/-approximate summary can be maintained so that, given a quantile query (/spl phi/,/spl epsi/), the data item at rank /spl lceil//spl phi/N/spl rceil/ may be approximately obtained within the rank error precision /spl epsi/N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different /spl phi/ and /spl epsi/ poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.
27094711
An approach to multiple attribute decision making based on fuzzy preference information on alternatives
This paper investigates the multiple attribute decision making problem with preference information on alternatives, in which multiple decision makers give their preference information in three forms, i.e., preference orderings, utility values and fuzzy preference relation. A new approach is presented to make use of both the decision makers' social fuzzy preference relation on alternatives and decision matrix to form an optimization model. The optimization model can be used to determine the attribute weights and rank the alternatives. The approach provides a new way to reflect the decision makers' social preference information and the decision matrix. Finally, an example is used to illustrate the proposed approach.
13853728
A power-optimized joint source channel coding for scalable video streaming over wireless channel
Scalable video streaming over wireless link is a very challenging task due to the time-varying characteristics of wireless channel and limited battery resource in handheld devices. This paper first describes a general approach for joint source channel coding (JSCC) with optimal power consumption. Then, an end-to-end power-optimized architecture for video streaming is presented. Therein the channel-adaptive hybrid UEP (unequal error protection) and ARQ (automatic repeat request) scheme is proposed to achieve the minimal power consumption. Simulation results demonstrate the effectiveness of our proposed scheme.