Abstract: This paper presents a space mapping algorithm for airfoil shape optimization enhanced with adjoint sensitivities. The surrogate-based algorithm utilizes low-cost derivative information obtained through adjoint sensitivities to improve the space mapping matching between a high-fidelity airfoil model, evaluated through expensive CFD simulations, and its fast surrogate. Here, the airfoil surrogate model is constructed though low-fidelity CFD simulations. As a result, the design process can be performed at a low computational cost in terms of the number of high-fidelity CFD simulations. The adjoint sensitivities are also exploited to speed up the surrogate optimization process. Our method is applied to a constrained drag minimization problem in two-dimensional inviscid transonic flow. The problem is solved for several low-fidelity model termination criteria. The results show that when compared with direct gradient-based optimization with adjoint sensitivities, the proposed approach requires 49-78% less computational cost while still obtaining a comparable airfoil design.

Abstract: This paper introduces a new sampling technique called Opposite-Center Learning (OCL) intended for convergence speedup of meta-heuristic optimization algorithms. It comprises an extension of Opposition-Based Learning (OBL), a simple scheme that manages to boost numerous optimization methods by considering the opposite points of candidate solutions. In contrast to OBL, OCL has a theoretical foundation – the opposite center point is defined as the optimal choice in pair-wise sampling of the search space given a random starting point. A concise analytical background is provided. Computationally the opposite center point is approximated by a lightweight Monte Carlo scheme for arbitrary dimension. Empirical results up to dimension 20 confirm that OCL outperforms OBL and random sampling: the points generated by OCL have shorter expected distances to a uniformly distributed global optimum. To further test its practical performance, OCL is applied to differential evolution (DE). This novel scheme for continuous optimization named Opposite-Center DE (OCDE) employs OCL for population initialization and generation jumping. Numerical experiments on a set of benchmark functions for dimensions 10 and 30 reveal that OCDE on average improves the convergence rates by 38% and 27% compared to the original DE and the Opposition-based DE (ODE), respectively, while remaining fully robust. Most promising are the observations that the accelerations shown by OCDE and OCL increase with problem dimensionality.

Abstract: Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup’s HIO, behave by introducing a reduced dimensionality problem allowing us to visualize convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm’s ability to converge to the globally optimal solution.

Abstract: A methodology for a rapid design optimization of integrated photonic couplers is presented. The proposed technique exploits variable-fidelity electromagnetic (EM) simulation models, additive response correction for accommodating the discrepancies between the EM models of various fidelities, and local response surface approximations for a fine tuning of the final design. A specific example of a 1,555 nm coupler is considered with an optimum design obtained at a computational cost corresponding to about 24 high-fidelity EM simulations of the structure.

Abstract: Discriminative learning of Restricted Boltzmann Machines has been recently introduced as an alternative to provide a self-contained approach for both unsupervised feature learning and classification purposes. However, one of the main problems faced by researchers interested in such approach concerns with a proper selection of its parameters, which play an important role in its final performance. In this paper, we introduced some meta-heuristic techniques for this purpose, as well as we showed they can be more accurate than a random search, that is commonly used by some works.

Abstract: The performance of cooperative coevolutionary algorithms for large-scale continuous optimization is significantly affected by the adopted decomposition of the search space. According to the literature, a typical decomposition in case of fully separable problems consists of adopting equally sized subcomponents for the whole optimization process (i.e. static decomposition). Such an approach is also often used for fully non-separable problems, together with a random-grouping strategy. More advanced methods try to determine the optimal size of subcomponents during the optimization process using reinforcement-learning techniques. However, the latter approaches are not always suitable in this case because of the non-stationary and history-dependent nature of the learning environment. This paper investigates a new Cooperative Coevolutionary algorithm, based on Differential Evolution, in which several decompositions are applied in parallel during short learning phases. The experimental results on a set of large-scale optimization problems show that the proposed method can lead to a reliable estimate of the suitability of each subcomponent size. Moreover, in some cases it outperforms the best static decomposition.

Abstract: Distributed environments with the decoupling of users from resource providers are generally termed as utility Grids. The paper focuses on the problems of efficient job flow distribution and scheduling in virtual organizations (VOs) of utility Grids while ensuring the VO stakeholders preferences and providing dependable strategies for resources utilization. An approach based on the combination of the cyclic scheduling scheme, backfilling and several heuristic procedures is proposed and studied. Comparative simulation results are introduced for different algorithms and heuristics depending on the resource domain composition and heterogeneity. Considered scheduling approaches provide different benefits depending on the VO scheduling objectives.The results justify the use of the proposed approaches in a broad range of the considered resource environment parameters.

Abstract: We propose the stochastic simplex bisection algorithm. It randomly selects one from a set of simplexes, bisects it, and replaces it with its two offspring. The selection probability is proportional to a score indicating how promising the simplex is to bisect. We generalize intervals to simplexes, rather than to hyperboxes, as bisection then only requires evaluating the function in one new point, which is somewhat randomized. Using a set of simplexes that partition the search space yields completeness and avoids redundancy. We provide an appropriate scale- and offset-invariant score definition and add an outer loop for handling hyperboxes. Experiments show that the algorithm is capable of exploring vast numbers of local optima, over huge ranges, yet finding the global one. The ease with which it handles quadratic functions makes it ideal for non-linear regression: it is here successfully applied to logistic regression. The algorithm does well, also when the number of function evaluations is severely restricted.

Abstract: Numerical methods for global optimization of the multidimensional multiextremal functions in the framework of the approach oriented at dimensionality reduction by means of the nested optimization scheme are considered. This scheme reduces initial multidimensional problem to a set of univariate subproblems connected recursively. That enables to apply efficient univariate algorithms for solving the multidimensional problems. The nested optimization scheme served as the source of many methods for optimization of Lipschitzian function. However, in all of them there is the problem of estimating the Lipschitz constant as the parameter of the function optimized and, as a consequence, of tuning to it the optimization method. In the methods proposed earlier, as a rule, a global estimate (related to whole search domain) is used whereas local Lipschitz constants in some subdomains can differ significantly from the global constant. It can slow down the optimization process considerably. To overcome this drawback in the article the finer estimates of a priori unknown Lipschitz constants taking into account local properties of the objective function are considered and used in the nested optimization scheme. The results of numerical experiments presented demonstrate the advantages of methods with mixed (local and global) estimates of Lipschitz constants in comparison with the use the global ones only.

Abstract: In this work the inverse problem of identification of structural stiffness coefficients of a damped spring-mass system is tackled. The problem is solved by using different versions of Ant Colony Optimization (ACO) metaheuristic solely or coupled with the Hooke-Jeeves (HJ) local search algorithm. The evaluated versions of ACO are based on a discretization procedure to deal with the continuous domain design variables together with different pheromone evaporation and deposit strategies and also on the frequency of calling the local search algorithm. The damage estimation is evaluated using noiseless and noisy synthetic experimental data assuming a damage configuration throughout the structure. The reported results show the hybrid method as the best choice when both rank-based pheromone deposit and a new heuristic information based on the search history are used.

Abstract: A procedure for low-cost multi-objective design optimization of antenna structures is discussed. The major stages of the optimization process include: (i) an initial reduction of the search space aimed at identifying its relevant subset containing the Pareto-optimal design space, (ii) construction—using sampled coarse-discretization electromagnetic (EM) simulation data—of the response surface approximation surrogate, (iii) surrogate optimization using a multi-objective evolutionary algorithm, and (iv) the Pareto front refinement. Our optimization procedure is demonstrated through the design of a planar quasi Yagi-Uda antenna. The final set of designs representing the best available trade-offs between conflicting objectives is obtained at a computational cost corresponding to about 172 evaluations of the high-fidelity EM antenna model.

Abstract: The paper describes methods for constructing the robust schedules using agent-based simulation. The measure of robustness represents the resistance of the schedule to random phenomena and we present the method for calculating robustness of the schedule. The procedure for creating the robust schedule combines standard solutions for planning and scheduling with computer simulation. It is described in detail and allows creation an executable robust schedule. Three different procedures for increasing the robustness (by changing the order of allocation of resources, by changing a plan and increasing time reserves) are short explained. The presented techniques were tested using real detailed simulation model of an existing container terminal.

Abstract: Trawl-doors have a large influence on the fuel consumption of fishing vessels. Design and optimization of trawl-doors using computational models are key factors in minimizing the fuel consumption. This paper presents an efficient optimization algorithm for the design of trawl-door shapes using computational fluid dynamic models. The approach is iterative and uses variable-fidelity models and space mapping. The algorithm is applied to the design of a multi-element trawl-door, involving four design variables controlling the angle of attack and the slat position and orientation. The results demonstrate that a satisfactory design can be obtained at a cost of a few iterations of the algorithm. Compared with direct optimization of the high-fidelity model and local response surface surrogate models, the proposed approach requires 79% less computational time while, at the same time, improving the design significantly (over 12% increase in the lift-to-drag ratio).

Abstract: Focal brachytherapy is a clinical procedure that can be used to treat low-risk prostate cancer with reduced side-effects compared to conventional brachytherapy. Current practice is to manually plan the placement of radioactive seeds inside the prostate to achieve a desired treatment dose. Problems with the current practice are that the manual planning is time-consuming and high doses to the urethra and rectum cause undesirable side-effects. To address this problem, we have designed an optimisation algorithm that constructs treatment plans which achieve the desired dose while minimizing dose to organs at risk. We also show that these seed plans are robust to post-operative movement of the seeds within the prostate.

Abstract: Statistically Similar Representative Volume Element (SSRVE) is used to simplify computational domain for microstructure representation of material in multiscale modelling. The procedure of SSRVE creation is based on optimization loop which allows to find the highest similarity between SSRVE and an original material microstructure. The objective function in this optimization is built upon computationally intensive numerical methods, including simulations of virtual material deformation, which is very time consuming. To avoid such long lasting calculations we propose to use the data farming approach to identification of SSRVE for Advanced High Strength Steels (AHSS) characterized by multiphase microstructure. The optimization method is based on a nature inspired approach which facilitates distribution and parallelization. The concept of SSRVE creation as well as the software architecture of the proposed solution is described in the paper in details. It is followed by examples of the results obtained for the identification of SSRVE parameters for DP steels which are widely exploited in modern automotive industry. Possible directions for further development and uses are described in the conclusions.