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A new approach is proposed for shape optimization of shells, where the requirements on geometrical properties, constructability, and mechanical performance are simultaneously considered in the problem formulation. The surface shape is modeled using a triangular Bézier patch to reduce the number of design variables, while the ability of generating moderately complex shape is maintained. The strain energy under specified loading condition is used to represent the mechanical performance, and the geometrical properties are quantitatively defined using algebraic invariants of the parametric surface. The developable surface that has high constructability is generated by assigning an appropriate constraint on an algebraic invariant. The effectiveness of the proposed approach is demonstrated through several numerical examples, and the characteristics of the optimal shapes under various constraints are discussed. [ABSTRACT FROM AUTHOR]

In this correspondence, a two-channel linear phase finite-impulse- response (FIR) quadrature mirror filter (QMF) bank minimax design problem is formulated as a nonconvex optimization problem so that a weighted sum of the maximum amplitude distortion of the filter bank, the maximum passband ripple magnitude and the maximum stopband ripple magnitude of the prototype filter is minimized subject to specifications on these performances. A modified filled function method is proposed for finding the global minimum of the nonconvex optimization problem. Computer numerical simulations show that our proposed design method is efficient and effective. [ABSTRACT FROM AUTHOR]

We provide an improved FPTAS for multiobjective shortest paths—a fundamental (NP-hard) problem in multiobjective optimization—along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain better approximate solutions to three related problems, multiobjective constrained [optimal] path and non-additive shortest path, that have important applications in QoS routing and in traffic optimization. We also show how to obtain a FPTAS to a natural generalization of the weighted multicommodity flow problem with elastic demands and values that models several realistic scenarios in transportation and communication networks. [ABSTRACT FROM AUTHOR]

The lattice Boltzmann method is an important technique for the numerical solution of partial differential equations because it has nearly ideal scalability on parallel computers for many applications. However, to achieve the scalability and speed potential of the lattice Boltzmann technique, the issues of data reusability in cache-based computer architectures must be addressed. Utilizing the two-dimensional diffusion equation, $T_t=\mu(T_{xx}T_{yy})$, this paper examines cache optimization for the lattice Boltzmann method in both serial and parallel implementations. In this study, speedups due to cache optimization were found to be 1.9–2.5 for the serial implementation and 3.6–3.8 for the parallel case in which the domain decomposition was optimized for stride-one access. In the parallel non-cached implementation, the method of domain decomposition (horizontal or vertical) used for parallelization did not significantly affect the compute time. In contrast, the cache-based implementation of the lattice Boltzmann method was significantly faster when the domain decomposition was optimized for stride-one access. Additionally, the cache-optimized lattice Boltzmann method in which the domain decomposition was optimized for stride-one access displayed superlinear scalability on all problem sizes as the number of processors was increased. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Nature-Inspired Techniques has been becoming the focus of research because they achieved the remarkable successes in optimization problems. The power of almost all Nature-inspired techniques comes from the fact that they imitate the best characteristic in nature. Nature-inspired algorithms are characterized by algorithmic operators mimicking computationally useful aspects of various natural phenomena. Nature inspired techniques such as swarm intelligence (SI), Nature Evolution and so on. It has demonstrated strong efficiency technique for solving complex problems and it provides optimum solution. This paper discusses a number of selected nature-inspired algorithms and various types of combinatorial optimization problems and it mainly focused on the unique attitude behind each of the techniques, their applications. [ABSTRACT FROM AUTHOR]