1722915
A Hybrid Estimation of Distribution Algorithm for CDMA Cellular System Design
While code division multiple access (CDMA) is becoming a promising cellular communication system, the design for a CDMA cellular system configuration has posed a practical challenge in optimisation. The study in this paper proposes a hybrid estimation of distribution algorithm (HyEDA) to optimize the design of a cellular system configuration. HyEDA is a two-stage hybrid approach built on estimation of distribution algorithms (EDAs), coupled with a K-means clustering method and a simple local search algorithm. Compared with the simulated annealing method on some test instances, HyEDA has demonstrated its superiority in terms of both the overall performance in optimisation and the number of fitness evaluations required.
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.
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.
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.
10598810
An orthogonal genetic algorithm for multimedia multicast routing
Many multimedia communication applications require a source to send multimedia information to multiple destinations through a communication network. To support these applications, it is necessary to determine a multicast tree of minimal cost to connect the source node to the destination nodes subject to delay constraints on multimedia communication. This problem is known as multimedia multicast routing and has been proved to be NP-complete. The paper proposes an orthogonal genetic algorithm for multimedia multicast routing. Its salient feature is to incorporate an experimental design method called orthogonal design into the crossover operation. As a result, it can search the solution space in a statistically sound manner and it is well suited for parallel implementation and execution. We execute the orthogonal genetic algorithm to solve two sets of benchmark test problems. The results indicate that for practical problem sizes, the orthogonal genetic algorithm can find near optimal solutions within moderate numbers of generations.
45612453
Power-minimized bit allocation for video communication over wireless channels
Video communication over wireless links using handheld devices is a challenging task due to the time-varying characteristics of the wireless channels and limited battery resources. Rate-distortion (RD) analysis plays a key role in video coding and communication systems, and usually the RD relation does not assume any power constraint. We investigate the relations of rate, distortion and power consumption. Based on those relations, we propose a power-minimized bit-allocation scheme considering the processing power, for source coding and channel coding, jointly with the transmission power. The total bits are allocated between source and channel coders, according to wireless channel conditions and video quality requirements, to minimize the total power consumption for a single user and a group of users in a cell, respectively. Simulation results show that our proposed joint power-control and bit-allocation scheme achieves high power savings compared to the conventional scheme.
15428033
On Optimal Physical Carrier Sensing: Theoretical Analysis and Protocol Design
Traditional Physical Carrier Sensing (PCS), which aims at eliminating hidden terminals in wireless networks, causes too many exposed terminals and deteriorates the throughput per user seriously. Some existing work has proven that an aggressive PCS can improve the throughput by balancing the tradeoff between hidden terminals and exposed terminals. However, little work has been conducted to optimize the PCS according to the network conditions. In this paper, we develop an analytical model to study the behaviors of a user in the aggressive PCS scenario, with which the optimal PCS threshold can be derived. Then, to avoid the complicated computation, we present a heuristic algorithm, in which the parameters required for PCS tuning are estimated by the carrier sensing mechanism employed in IEEE 802.11. Simulation results show that the proposed heuristic algorithm can make the PCS threshold approach to the theoretical optimal one. Moreover, the proposed heuristic algorithm can obtain significant throughput gain compared to the traditional PCS threshold setting solutions.
14103418
Front Tracking: A Parallelized Approach for Internal Boundaries and Interfaces
Internal boundaries and interfaces are an important part of many fluid and solid modeling problems. Front Tracking contains a general interface framework, closely related to the non-manifold geometry used in CAD solid modeling packages, to support numerical simulation of such fluid problems. It can thus be considered to be a systematic application of the ideas of computational geometry to computational fluid dynamics. It is based on the principle that finite differences behave best when applied to differentiable functions, and that weak derivatives of nondifferentiable functions can be replaced by regularized expressions such as jump conditions. Front Tracking offers superior resolution for fluid problems with important discontinuities and interfaces, and in some cases, it has provided the unique method to obtain correct answers. Here we present Computer Science issues which have contributed to the success of Front Tracking: software design and organization — modularity, data structures and data hiding.
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.