Abstract: One of the main tasks involving the development of a new spacecraft is how to distribute its electronic equipment over its structural panels. This problem is first addressed in the conception phase of the design and is traditionally carried out by a group of system engineers. It is a multidisciplinary task since structural, thermal, dynamics, and integration issues, must all be taken into account simultaneously. Usually, the initial positioning is done based on the engineers’ experience, followed by an analysis stage (thermal, structural, etc.) in which the design performance and constraints are verified. This process takes time and hence, as soon as a good feasible design is found, it is taken as the baseline. This precludes a broad exploration of the conceptual design space, which usually leads to a suboptimal layout design. In this paper the main features of a multi-objective methodology are presented which were developed to automatically find solutions for a three-dimensional layout of equipment in spacecraft. It includes mass, inertia, thermal and subsystem requirements and geometric constraints using a multi-objective approach that combines CAD and optimization tools in an integrated environment. As a case study, the methodology was applied to the layout optimization of the Brazilian Multi-Mission Space Platform (MMP) equipment. The main results are presented.

Abstract: Common carrier pipelines are the best suited mode for transporting large volumes of petroleum products from production areas to long-distance terminals. In such pipelines, batches of refined petroleum products are pumped back-to-back, without any physical barrier separating them. The problem addressed in this paper deals with the scheduling of a real world multi-product, single source pipeline system that must supply the products to several offtake terminals. Most contributions on the scheduling of single-source multiproduct pipeline operations deal with the sequence deliveries in distribution terminals i.e., at any time only one terminal is connected to the pipeline. Practically, pipeline operators usually carry out simultaneous deliveries to multiple terminals to cut down the number of pipeline stoppages and pump switchings so as to reduce the energy consumed for resuming flow in idle pipeline segments, and the pump maintenance costs. This paper introduces a novel continuous mathematical approach, mixed integer linear programming to solve the short-term operational planning by allowing the execution of simultaneous deliveries to multiple receiving terminals during a pumping operation. Contrarily to previous continuous approaches that perform the pipeline input/output operations in two hierarchical stages, the new formulation aims to find both of them in a single step. The objective of this work is to find the optimal sequence of input and output operations that satisfy terminal requirements at minimum total costs. As compared to previous works, significantly improvement in solution quality has been achieved. Especially, the proposed formulation leads to better utilization of the pipeline capacity, and consequently, a substantial reduction in the amount of required time for satisfying all of the specified product deliveries, i.e., the pipeline running time. The main results are presented.

Abstract: Various constrained problem formulations for the optimization of an electro-thermal wing anti-icing system in both running-wet and evaporative regimes are presented. The numerical simulation of the system is performed by solving the conjugate heat transfer problem between the fluid and solid domains. The optimization goal is to reduce the energy use and power demand of the anti-icing system while ensuring a safe protection. The formulations are carefully proposed from the physical and mathematical viewpoints; their performance is assessed by means of several numerical test cases to discern the most promising for each regime. The design optimization is conducted using the mesh adaptive direct search algorithm using quadratic and statistical surrogate models in the search step. The influence of the models on the convergence speed and the quality of the obtained design solutions is investigated.

Abstract: Optimal sensor placement problems are considered for a thermoelastic solid body. The main motivation is the real-time capable prediction of the displacement of the tool center point (TCP) in a machine tool by temperature measurements. A reduced order model based on proper orthogonal decomposition is used to describe the temperature field. The quality of the TCP displacement estimation is measured in terms of the associated covariance matrix. Based on this criterion, a sequential placement algorithm is described which stops when a certain prediction quality is reached. Numerical tests are provided.

Abstract: The unavoidable presence of uncertainties poses several difficulties to the numerical treatment of optimization tasks. In this paper, we discuss a general framework attacking the additional computational complexity of the treatment of uncertainties within optimization problems considering the specific application of optimal aerodynamic design. Appropriate measure of robustness and a proper treatment of constraints to reformulate the underlying deterministic problem are investigated. In order to solve the resulting robust optimization problems, we propose an efficient methodology based on a combination of adaptive uncertainty quantification methods and optimization techniques, in particular generalized one-shot ideas. Numerical results investigating the reliability and efficiency of the proposed method as well as the influence of different robustness measures on the resulting optimized shape will be presented.

Abstract: This paper describes a modified variable complexity modeling (MVCM) framework that uses a neural network to replace the Taylor series after several warm-up iterations. The MVCM framework with an additive scaling function is most efficient in terms of high-fidelity function evaluation savings compared with traditional variable complexity modeling (VCM) among multiplicative and hybrid scaling functions. The MVCM framework achieves 59.1 and 68.6 % savings in high-fidelity function evaluations for one-dimensionally and two-dimensionally constrained problems, respectively, compared with the VCM method. The MVCM framework provides a larger trust region than the VCM due to the global behavior of neural networks. The MVCM solution also converges closely to the high-fidelity function. The MVCM framework is integrated with an in-house low-fidelity aircraft design synthesis program and a high-fidelity analysis (AADL3D) for the conceptual design of multidisciplinary regional jet aircraft (RJA) to enhance the optimal RJA configuration compared with low-fidelity analysis results. The optimal RJA wing configuration using the MVCM framework provides more realistic and reasonable configurations compared to the results of low-fidelity analysis with a short turnaround time.

Abstract: In this paper we present a new approach to solve the DC (n − 1) security constrained optimal power flow (SCOPF) problem by a structure exploiting interior point solver. Our approach is based on a reformulation of the linearised SCOPF model, in which most matrices that need to be factorized are constant. Hence, most factorizations and a large number of back-solve operations only need to be performed once. However, assembling the Schur complement matrix remains expensive in this scheme. To reduce the effort, we suggest using a preconditioned iterative method to solve the corresponding linear system. We suggest two main schemes to pick a good and robust preconditioner based on combining different “active” contingency scenarios of the SCOPF model. These new schemes are implemented within the object-oriented parallel solver (OOPS), a structure-exploiting primal-dual interior-point implementation. We give results on several SCOPF test problems. The largest example contains 500 buses. We compare the results from the original interior point method (IPM) implementation in OOPS and our new reformulation.

Abstract: This paper deals with the deterministic global optimization of hybrid distillation/melt-crystallization processes for closely boiling mixtures. An algorithm is presented that exploits the problem specific structure of continuous, counter-current distillation to reduce the domain of the corresponding mixed-integer nonlinear program (MINLP). We apply a bound tightening technique based on the explicit computation of extreme column solution profiles, which enclose all possible solutions of the distillation column model. A relaxed MINLP model formulation is then used to exclude several non-optimal and infeasible column configurations. The numerical performance of the proposed algorithm is demonstrated on a test series of stand-alone distillation column processes and hybrid separation processes.

Abstract: Computation of exact ellipsoidal bounds on the state trajectories of discrete-time linear systems that have time-varying or time-invariant linear fractional parameter uncertainties and ellipsoidal uncertainty in the initial state is known to be NP-hard. This paper proposes three algorithms to compute ellipsoidal bounds on such a state trajectory set and discusses the tradeoffs between computational complexity and conservatism of the algorithms. The approach employs linear matrix inequalities to determine an initial estimate of the ellipsoid that is refined by the subsequent application of the skewed structured singular value $$ u $$ . Numerical examples are used to illustrate the application of the proposed algorithms and to compare the differences between them, where small conservatism for the tightest bounds is observed.

Abstract: Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a scenario which formulates the problem of optimizing a computationally expensive black-box functions. In such problems, there will often exist candidate designs which cause the simulation to fail, and this can degrade the optimization effectiveness. To address this issue, this paper proposes a new optimization algorithm which incorporates classifiers into the optimization search. The classifiers predict which candidate design are expected to cause the simulation to fail, and their prediction is used to bias the search towards valid designs, namely, for which the simulation is expected to succeed. However, the effectiveness of this approach strongly depends on the type of metamodels and classifiers being used, but due to the high cost of evaluating the simulation-based objective function it may be impractical to identify by numerical experiments the most suitable types of each. Leveraging on these issues, the proposed algorithm offers two main contributions: (a) it uses ensembles of both metamodels and classifiers to benefit from a diversity of predictions of different metamodels and classifiers, and (b) to improve the search effectiveness, it continuously adapts the ensembles’ topology during the search. The performance of the proposed algorithm was evaluated using an engineering problem of airfoil shape optimization. Performance analysis of the proposed algorithm using an engineering problem of airfoil shape optimization shows that: (a) incorporating classifiers into the search was an effective approach to handle simulation failures (b) using ensembles of metamodels and classifiers, and updating their topology during the search, improved the search effectiveness in comparison to using a single metamodel and classifier, and (c) it is beneficial to update the topology of the metamodel ensemble in all problem types, and it is beneficial to update the classifier ensemble topology in problems where simulation failures are prevalent.

Abstract: Maximizing economical asset of oil reservoirs is a simulation-based optimization involving large-scale simulation models. In this work we propose the use of reduced-order models for solving optimization problems in oil reservoir simulation using a Lagrangian barrier method for the treatment of nonlinear inequality constraints. The optimization with reduced-order models is done by employing a trust-region proper orthogonal decomposition (TRPOD) algorithm. In addition to the POD method, we also build a reduced-order model based on a discrete empirical interpolation method. In the algorithm, the first-order gradient of the objective function is computed by using the adjoint method, while the inverse of the second-order gradient is approximated using the BFGS method. The reduced-order models involve both the forward (state) and backward (adjoint) equations. Three optimization case examples in production optimization of oil reservoirs are used to study the method. They show that the TRPOD method works efficiently while simultaneously honoring the nonlinear constraints.

Abstract: This paper proposes a synergetic approach to design a planar parallel robot with its control system. In this proposal, the design problem is stated as a dynamic optimization problem with dynamic and static constraints on both the robot parameters and the control input to the robot. Control parameterization via PID controllers is used to rewrite the dynamic optimization problem as a nonlinear programming problem, which is solved by using a hybrid gradient-evolutionary optimization technique. The dynamic optimization problem presents singularity regions in the design space requiring the use of the proposed hybrid gradient-evolutionary optimization technique. The rationale behind the proposed hybrid algorithm lies in using a exploratory search mechanism for finding the initial guess to the fine search mechanism, which is used to search in a local region of a solution. We discuss both the results of the proposed optimization technique and the experimental results of the robot designed with the proposed approach. In addition, the result provided by the proposed synergetic design approach is compared with a sequential design approach, showing the advantages of the synergetic approach.

Abstract: In Intel’s “Tick-Tock” roadmap a new processor is first manufactured in the most advanced stable process technology, followed in a 1-year delay by introducing chips comprising same microarchitecture but manufactured in a newer scaled process technology. Tick-Tock is enabled by the automation of chip’s layout migration from an older into a newer process technology, known as hard-IP reuse. This is a very challenging computational task, involving billions of polygons. Migration algorithms have been thoroughly studied and implemented in the past but their computational capabilities fall short compared to today’s demand. We describe a hierarchy-driven computationally efficient algorithm for cell-based layout conversion, used by Intel in its Tick-Tock roadmap. The algorithm transforms the full chip conversion problem into successive problems of significantly smaller size, having feasible solutions if and only if the full chip problem does. The proposed algorithm preserves the design intent, its uniformity, portability and maintainability, a key for the success of large-scale projects.

Abstract: Noble Energy produces and sells tens of thousands of barrels of oil a day in the Wattenberg field in northeastern Colorado, one of the largest natural gas deposits in the United States. This paper describes a new mathematical model that was built and implemented to support the company’s business decisions regarding its current and future sales, dispatch, and transportation operations. The corresponding multicriteria optimization model is formulated and solved as a multi-period, multi-objective mixed-integer program that considers the maximization of revenue and sales, and the avoidance of temporary production shut-ins and sell-outs to guarantee long-term contractual obligations with its partnering well owners, haulers, and markets. A theoretical tradeoff analysis is presented to validate model decisions with current operational practice, and a small computational case study on an original data set demonstrates the use of this model to find efficient dispatch schedules and gain further insights into the tradeoffs between the different decision criteria.

Abstract: This paper proposes a novel particle filter, namely, the auxiliary iterated extended Kalman particle filter (AIEKPF). To generate the importance density, based on the auxiliary particle filtering (APF) technique the proposed filter uses the iterated extended Kalman filter (IEKF) to integrate the latest measurements into state transition density. This new filter can match the posterior density well, because of the robustness of the APF and the importance density generated by the IEKF. The performance of the presented particle filter is evaluated by two different estimation problems with the noise of Gaussian distribution and Gamma distribution, respectively. The experimental results illustrate that the AIEKPF is superior to the extended Kalman filter and some existing particle filters, such as the standard particle filter (PF), the extended Kalman particle filter, the unscented Kalman particle filter (UKPF) and the auxiliary extended Kalman particle filter, where the number of particles is relatively small, such as 200 and 1,000. However, with an increase of particles, the superiority of the proposed method may decline compared with the PF and APF as showed in the experiments. Also, the AIEKPF has less running time than the UKPF under the same conditions, and from the viewpoint of the average effective sample sizes, it is clear that the AIEKPF has the slightest degeneracy in all filters presented in the experiments.

Abstract: A new recursive algorithm for the least squares problem subject to linear equality and inequality constraints is presented It is applicable for problems with a large number of inequalities The algorithm combines three types of recursion: time-, order-, and active-set-recursion Each recursion step has time-complexity \(O(d^2)\), where \(d\) is the dimension of the data vectors An \(O(d^2)\)-refreshment of the corresponding inverse matrices after each time-period of length \(d\) makes the algorithm numerically very stable, such that it can handle arbitrarily many data vectors without significant rounding errors Processing a new data vector (which usually only slightly changes the instance of the optimization problem) has time complexity \(O(d^2)\), provided that the active set method only requires \(O(1)\) steps for the update until the optimum is found In a series of examples with randomly generated data sets and with either convex constraints or with randomly generated linear constraints, the set of active constraints remains relatively stable after the inclusion of each new data vector

Abstract: As Editor-in-Chief of OPTE, I am delighted to announce the inauguration of the Howard Rosenbrock Prize. The prize will honor the best paper published in OPTE each year. It is sponsored by Springer, and the winner will receive USD $500 and a plaque. The recipient for 2014 is Jason E. Hicken of the Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute. His winning article is entitled Inexact Hessian-vector products in reduced-space differential-equation constrained optimization (Hicken 2014). The citation reads: This article represents a perfect example of work that bridges applied mathematics and engineering. Applied mathematicians have presented algorithms of this type before, but they tend to be too intrusive or cumbersome for engineers to use. On the engineering side, researchers and practitioners have been using off-theshelf optimizers that employ the reduced space approach, which is subject to a bottleneck when scaling up the number of design variables. The proposed approach is a combination of these two approaches, and it is shown to be scalable and applicable to engineering design problems. The method introduced in this paper could pave the way to a new class of efficient parallel nonlinear optimizers that have the potential to have a large impact on both the engineering and applied mathematics optimization communities.

Abstract: This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models. The modificati ...

Abstract: We focus on a particular class of optimal design problems in elasticity where the objective function depends on the stresses along the boundary to be optimized. This is an issue of interest with many engineering applications. This work describes a gradient-type method to solve the finite element numerical approximation of such problems. The descent direction of the discrete cost functional is computed as a discrete projection of the continuous gradient formula, which is derived systematically from shape derivatives and a careful local coordinates calculus. The computational procedure is presented together with an illustrative example, namely the optimization of a cross-sectional tunnel vault immersed in a linearly elastic terrain to obtain uniform compression along the vault.

Abstract: A predictor–corrector interior-point method is developed in order to deal with the AC active and reactive optimal power flow problem. Voltage rectangular coordinates are adopted instead of polar ones, since they allow nonlinear corrections for the primal and dual feasibility conditions and not only for the complementary constraints as in the traditional nonlinear programming methods. A new heuristic is proposed to handle voltage magnitude constraints. Computational experiments for IEEE test systems and a real Brazilian system are presented and show the advantages of the proposed approach.

Abstract: We present an asymptotic approach to find optimal rotations of orthotropic material inclusions inside an isotropic linear elastic matrix. We compute approximate optimal solutions with respect to compliance and a stress based cost functional. We validate the local and global quality of the candidate solutions by means of finite element based parametric optimization algorithms. In particular, we devise a lower bound algorithm based on the free material optimization approach. Several numerical experiments are performed for different traction scenarios.

Abstract: We present a new algorithm for behavioral targeting of banner advertisements. We record different user’s actions such as clicks, search queries and pageviews. We use the collected information to estimate in real time the probability of a click on a banner. Each click on a banner generates a profit. Our goal is to maximize the overall profit. We use a naive Bayesian model. We keep track of the click frequencies of the different banners under the additional information provided by the actions that each user has performed. We apply our strategy on real data in which we simply use the hours during which a user is connected as a feature. We describe the results obtained on these real data that give support to the effectiveness of our strategy. Moreover we describe some heuristics to improve the estimate of the click frequencies and to avoid displaying the same banner to the same user too many times.

Abstract: We present a new and algebraic approach to the optimal damping of servo axes during commissioning. The approach is based on control of root loci of the denominator of the transfer function using algebraic elimination. The results are either explicit formulas for simple systems or descriptions of the optima via roots of univariate polynomials. The power of the result is demonstrated in examples.

Abstract: We study integer programming formulations for the 2-schemes strip cutting problem with a sequencing constraint (2-SSCPsc) considered by Rinaldi and Franz. The 2-SSCPsc arises in the context of corrugated cardboard production, which involves cutting strips of large lengths into rectangles of at most (usually) two different lengths. Because of buffer restrictions, in the 2-SSCPsc these strips must be sequenced in such a way that, at any moment, at most two types of items are in production and not completed yet. This problem is NP-hard. We present four integer programming formulations for this problem, and our computational experiments with real-life instances show that one of them has a very tight integrality gap. We propose a heuristic procedure based on this formulation and present computational experience showing that this procedure finds very good primal solutions in small running times.

Abstract: In this paper, a new hybrid algorithm (FP–NSGA-II) is proposed by combining the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II) with a simple front prediction algorithm. Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. FP–NSGA-II is similar to the original NSGA-II but generates better offsprings. This is achieved by using a prediction operator which utilizes the direction in the decision variable space between each solution in the first front and the nearest neighbour solution in the second front, in order to extrapolate future chromosomes. This enables the addition of solutions that are closer to the true Pareto front into the new generation. To assess the performance of the proposed approach, eight benchmark two-objective test problems and four three-objective test problems are used to compare FP–NSGA-II with NSGA-II. In addition, a three-objective heat exchanger network problem is examined to show the potential application of FP–NSGA-II in real-life problems. Results indicate that the FP–NSGA-II improves upon the performance of NSGA-II for a variety of benchmark test problems exhibiting different characteristics. In addition, a similar front prediction algorithm could also be easily integrated with other evolutionary algorithms to enhance its performance.

Abstract: It is known that the use of passive energy dissipation devices, as friction dampers, reduces significantly the dynamic response of structures subjected to dynamic actions. However, the parameters of each damper as well as the best placement of these devices remain difficult to determine. Although some studies on optimization of tuned mass damper and viscous/viscoelastic dampers are being developed, works on optimum use of friction dampers is still lacking. Thus, in this paper, the simultaneous optimization of force and placement of friction dampers is proposed. To solve this optimization problem, the recently developed firefly algorithm is employed, which is able to deal with non-convex optimization problems, involving mixed discrete and continuous variables. For illustration purposes, two common footbridges are analyzed, in which the cost function is to minimize the maximum acceleration of the structures, whereas forces and positions of friction dampers are the design variables. The results showed that the proposed method was able to determine the optimum friction forces of each damper as well as their best positions in the structures. The maximum acceleration was reduced in more than 95 % for the Warren truss footbridge, with three friction dampers, and in more than 92 % for the Pratt truss footbridge, with only two friction dampers. In addition, the proposed methodology is quite general and it is believed that it can be recommended as an effective tool for optimum design of friction dampers for structural response control. Thus, this paper shows that the design of friction dampers can be done in a safe and economic way.

Abstract: Filter networks are a powerful tool for reducing image processing time and maintaining high image quality. They are composed of sparse sub-filters whose high sparsity ensures fast image processing. The filter network design is related to solving a sparse optimization problem where a cardinality constraint bounds below the sparsity level. In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. Even when disregarding the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers, each of which is singular and non-isolated. The cardinality constraint makes the problem even more difficult to solve. An approach for approximately solving the cardinality-constrained MLLS problem is presented. It is then applied to solving a bi-criteria optimization problem in which both the time and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.

Abstract: The design of many real-life engineering systems involves optimization according to multiple, often conflicting, objectives. In this paper, an algorithm called spreading multi-objective particle swarm optimizer (SMOPSO) is developed and tested for optimization problems with two objectives. The motivation for SMOPSO is to promote a high diversity of solutions found in two-objective particle swarm optimization. This is attempted through the use of a spreading function based on neighboring particle positions and an archive controller which discriminates based on particle spacing. The spreading function directs non-dominated particles away from their nearest neighbor, aiming for evenly-spaced solutions as particles “spread out”. To test if such an approach can indeed improve Pareto front diversity, a performance comparison of SMOPSO is made to two benchmark algorithms. Preliminary results suggest the proposed algorithm may improve the diversity of solutions for a limited selection of optimization problems, but at the expense of other important measures of performance which is discussed in this paper. SMOPSO’s performance degrades for more difficult optimization problems, such those with multiple fronts and narrow global minima. An example application of SMOPSO to a theoretical, two-objective high-speed planing craft design problem is also given.

Abstract: With the target of optimizing CO2 sequestration in underground reservoirs, we investigate constrained optimal control problems with partially miscible two-phase flow in porous media Our objective is to maximize the amount of trapped CO2 in an underground reservoir after a fixed period of CO2 injection, while time-dependent injection rates in multiple wells are used as control parameters We describe the governing two-phase two-component Darcy flow PDE system, formulate the optimal control problem and derive the continuous adjoint equations For the discretization we apply a variant of the so-called BOX method, a locally conservative control-volume FE method that we further stabilize by a periodic averaging feature to reduce oscillations The timestep-wise Lagrange function of the control problem is implemented as a variational form in Sundance, a toolbox for rapid development of parallel FE simulations, which is part of the HPC software Trilinos We discuss the BOX method and our implementation in Sundance The MPI parallelized Sundance state and adjoint solvers are linked to the interior point optimization package IPOPT, using limited-memory BFGS updates for approximating second derivatives Finally, we present and discuss different types of optimal control results