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 Subjects -> ENGINEERING (Total: 1961 journals)     - CHEMICAL ENGINEERING (153 journals)    - CIVIL ENGINEERING (149 journals)    - ELECTRICAL ENGINEERING (81 journals)    - ENGINEERING (1114 journals)    - ENGINEERING MECHANICS AND MATERIALS (292 journals)    - HYDRAULIC ENGINEERING (46 journals)    - INDUSTRIAL ENGINEERING (52 journals)    - MECHANICAL ENGINEERING (74 journals) ENGINEERING (1114 journals)            First | 2 3 4 5 6 7 8 9 | Last
 International Journal of Manufacturing Research       (Followers: 6) International Journal of Manufacturing Technology and Management       (Followers: 8) International Journal of Materials and Product Technology       (Followers: 4) International Journal of Mathematical Education in Science and Technology       (Followers: 7) International Journal of Mathematics in Operational Research       (Followers: 1) International Journal of Medical Engineering and Informatics       (Followers: 5) International Journal of Micro Air Vehicles       (Followers: 4) International Journal of Microwave and Wireless Technologies       (Followers: 1) International Journal of Microwave Science and Technology       (Followers: 2) International Journal of Mobile Network Design and Innovation       (Followers: 3) International Journal of Multiphase Flow       (Followers: 2) International Journal of Nanomanufacturing       (Followers: 1) International Journal of Nanoscience       (Followers: 1) International Journal of Nanotechnology       (Followers: 5) International Journal of Navigation and Observation       (Followers: 5) International Journal of Network Management International Journal of Nonlinear Sciences and Numerical Simulation       (Followers: 1) International Journal of Numerical Methods for Heat & Fluid Flow       (Followers: 7) International Journal of Optics       (Followers: 1) International Journal of Organisational Design and Engineering       (Followers: 9) International Journal of Pattern Recognition and Artificial Intelligence       (Followers: 6) International Journal of Pavement Engineering       (Followers: 2) International Journal of Physical Modelling in Geotechnics       (Followers: 3) International Journal of Plasticity       (Followers: 6) International Journal of Plastics Technology International Journal of Polymer Analysis and Characterization       (Followers: 4) International Journal of Polymer Science       (Followers: 16) International Journal of Precision Engineering and Manufacturing       (Followers: 6) International Journal of Precision Technology International Journal of Pressure Vessels and Piping       (Followers: 2) International Journal of Production Economics       (Followers: 10) International Journal of Quality and Innovation       (Followers: 3) International Journal of Quality Engineering and Technology       (Followers: 2) International Journal of Quantum Information International Journal of Rapid Manufacturing       (Followers: 2) International Journal of Reliability, Quality and Safety Engineering       (Followers: 4) International Journal of Renewable Energy Technology       (Followers: 8) International Journal of Robust and Nonlinear Control       (Followers: 2) International Journal of Science Engineering and Advance Technology International Journal of Sediment Research       (Followers: 1) International Journal of Self-Propagating High-Temperature Synthesis       (Followers: 2) International Journal of Signal and Imaging Systems Engineering International Journal of Six Sigma and Competitive Advantage International Journal of Social Robotics       (Followers: 1) International Journal of Software Engineering and Knowledge Engineering       (Followers: 1) International Journal of Space Science and Engineering       (Followers: 2) International Journal of Speech Technology       (Followers: 3) International Journal of Spray and Combustion Dynamics       (Followers: 6) International Journal of Surface Engineering and Interdisciplinary Materials Science       (Followers: 1) International Journal of Surface Science and Engineering       (Followers: 7) International Journal of Sustainable Engineering       (Followers: 7) International Journal of Sustainable Manufacturing       (Followers: 4) International Journal of Systems Assurance Engineering and Management International Journal of Systems, Control and Communications       (Followers: 2) International Journal of Technology Management and Sustainable Development       (Followers: 1) International Journal of Technology Policy and Law       (Followers: 4) International Journal of Telemedicine and Applications       (Followers: 2) International Journal of Thermal Sciences       (Followers: 5) International Journal of Thermodynamics       (Followers: 2) International Journal of Turbo & Jet-Engines International Journal of Ultra Wideband Communications and Systems International Journal of Vehicle Autonomous Systems       (Followers: 1) International Journal of Vehicle Design       (Followers: 6) International Journal of Vehicle Information and Communication Systems       (Followers: 2) International Journal of Vehicle Noise and Vibration       (Followers: 3) International Journal of Vehicle Safety       (Followers: 4) International Journal of Vehicular Technology       (Followers: 2) International Journal of Virtual Technology and Multimedia       (Followers: 4) International Journal of Wavelets, Multiresolution and Information Processing International Journal on Artificial Intelligence Tools       (Followers: 4) International Nano Letters       (Followers: 6) International Review of Applied Sciences and Engineering Inverse Problems in Science and Engineering       (Followers: 2) Ionics IPTEK The Journal for Technology and Science IRBM News Irrigation and Drainage Systems ISA Transactions       (Followers: 1) ISRN - International Scholarly Research Notices       (Followers: 69) ISRN Nanotechnology ISRN Signal Processing ISRN Thermodynamics IT Professional       (Followers: 3) Journal of Biosensors & Bioelectronics       (Followers: 1) Journal of Advanced Manufacturing Systems       (Followers: 7) Journal of Aerosol Science       (Followers: 2) Journal of Aerospace Engineering       (Followers: 142) Journal of Alloys and Compounds       (Followers: 7) Journal of Analytical and Applied Pyrolysis       (Followers: 3) Journal of Analytical Science & Technology       (Followers: 4) Journal of Analytical Sciences, Methods and Instrumentation       (Followers: 1) Journal of Applied Analysis Journal of Applied and Industrial Sciences Journal of Applied Logic Journal of Applied Physics       (Followers: 168) Journal of Applied Probability       (Followers: 6) Journal of Applied Research and Technology Journal of Applied Science and Technology Journal of Applied Sciences       (Followers: 5) Journal of Architectural Engineering       (Followers: 6)
 Journal of Global Optimization    [6 followers]  Follow        Hybrid journal (It can contain Open Access articles)      ISSN (Print) 1573-2916 - ISSN (Online) 0925-5001      Published by Springer-Verlag  [2209 journals]   [SJR: 1.149]   [H-I: 46]
• A modified DIRECT algorithm with bilevel partition
• Abstract: Abstract It has been pointed out by Jones D. R. that the DIRECT global optimization algorithm can quickly get close to the basin of the optimum but takes longer to achieve a high degree of accuracy. In this paper, we introduce a bilevel strategy into a modifed DIRECT algorithm to overcome this shortcoming. The proposed algorithm is proved to be convergent globally. Numerical results show that the proposed algorithm is very promising.
PubDate: 2014-11-01

• Branch-and-Sandwich: a deterministic global optimization algorithm for
optimistic bilevel programming problems. Part II: Convergence analysis and
numerical results
• Abstract: Abstract In the first part of this work, we presented a global optimization algorithm, Branch-and-Sandwich, for optimistic bilevel programming problems that satisfy a regularity condition in the inner problem (Kleniati and Adjiman in J Glob Optim, 2014). The proposed approach can be interpreted as the exploration of two solution spaces (corresponding to the inner and the outer problems) using a single branch-and-bound tree, where two pairs of lower and upper bounds are computed: one for the outer optimal objective value and the other for the inner value function. In the present paper, the theoretical properties of the proposed algorithm are investigated and finite $$\varepsilon$$ -convergence to a global solution of the bilevel problem is proved. Thirty-four problems from the literature are tackled successfully.
PubDate: 2014-11-01

• Handelman’s hierarchy for the maximum stable set problem
• Abstract: Abstract The maximum stable set problem is a well-known NP-hard problem in combinatorial optimization, which can be formulated as the maximization of a quadratic square-free polynomial over the (Boolean) hypercube. We investigate a hierarchy of linear programming relaxations for this problem, based on a result of Handelman showing that a positive polynomial over a polytope with non-empty interior can be represented as conic combination of products of the linear constraints defining the polytope. We relate the rank of Handelman’s hierarchy with structural properties of graphs. In particular we show a relation to fractional clique covers which we use to upper bound the Handelman rank for perfect graphs and determine its exact value in the vertex-transitive case. Moreover we show two upper bounds on the Handelman rank in terms of the (fractional) stability number of the graph and compute the Handelman rank for several classes of graphs including odd cycles and wheels and their complements. We also point out links to several other linear and semidefinite programming hierarchies.
PubDate: 2014-11-01

• A penalty approximation method for a semilinear parabolic double obstacle
problem
• Abstract: In this work, we present a novel power penalty method for the approximation of a global solution to a double obstacle complementarity problem involving a semilinear parabolic differential operator and a bounded feasible solution set. We first rewrite the double obstacle complementarity problem as a double obstacle variational inequality problem. Then, we construct a semilinear parabolic partial differential equation (penalized equation) for approximating the variational inequality problem. We prove that the solution to the penalized equation converges to that of the variational inequality problem and obtain a convergence rate that is corresponding to the power used in the formulation of the penalized equation. Numerical results are presented to demonstrate the theoretical findings.
PubDate: 2014-11-01

• Fast calculation of multiobjective probability of improvement and expected
improvement criteria for Pareto optimization
• Abstract: The use of surrogate based optimization (SBO) is widely spread in engineering design to reduce the number of computational expensive simulations. However, “real-world” problems often consist of multiple, conflicting objectives leading to a set of competitive solutions (the Pareto front). The objectives are often aggregated into a single cost function to reduce the computational cost, though a better approach is to use multiobjective optimization methods to directly identify a set of Pareto-optimal solutions, which can be used by the designer to make more efficient design decisions (instead of weighting and aggregating the costs upfront). Most of the work in multiobjective optimization is focused on multiobjective evolutionary algorithms (MOEAs). While MOEAs are well-suited to handle large, intractable design spaces, they typically require thousands of expensive simulations, which is prohibitively expensive for the problems under study. Therefore, the use of surrogate models in multiobjective optimization, denoted as multiobjective surrogate-based optimization, may prove to be even more worthwhile than SBO methods to expedite the optimization of computational expensive systems. In this paper, the authors propose the efficient multiobjective optimization (EMO) algorithm which uses Kriging models and multiobjective versions of the probability of improvement and expected improvement criteria to identify the Pareto front with a minimal number of expensive simulations. The EMO algorithm is applied on multiple standard benchmark problems and compared against the well-known NSGA-II, SPEA2 and SMS-EMOA multiobjective optimization methods.
PubDate: 2014-11-01

• Interior Epigraph Directions method for nonsmooth and nonconvex
optimization via generalized augmented Lagrangian duality
• Abstract: We propose and study a new method, called the Interior Epigraph Directions (IED) method, for solving constrained nonsmooth and nonconvex optimization. The IED method considers the dual problem induced by a generalized augmented Lagrangian duality scheme, and obtains the primal solution by generating a sequence of iterates in the interior of the dual epigraph. First, a deflected subgradient (DSG) direction is used to generate a linear approximation to the dual problem. Second, this linear approximation is solved using a Newton-like step. This Newton-like step is inspired by the Nonsmooth Feasible Directions Algorithm (NFDA), recently proposed by Freire and co-workers for solving unconstrained, nonsmooth convex problems. We have modified the NFDA so that it takes advantage of the special structure of the epigraph of the dual function. We prove that all the accumulation points of the primal sequence generated by the IED method are solutions of the original problem. We carry out numerical experiments by using test problems from the literature. In particular, we study several instances of the Kissing Number Problem, previously solved by various approaches such as an augmented penalty method, the DSG method, as well as several popular differentiable solvers. Our experiments show that the quality of the solutions obtained by the IED method is comparable with (and sometimes favourable over) those obtained by the differentiable solvers.
PubDate: 2014-11-01

• A study of singular spectrum analysis with global optimization techniques
• Abstract: Singular spectrum analysis has recently become an attractive tool in a broad range of applications. Its main mechanism of alternating between rank reduction and Hankel projection to produce an approximation to a particular component of the original time series, however, deserves further mathematical justification. One paramount question to ask is how good an approximation that such a straightforward apparatus can provide when comparing to the absolute optimal solution. This paper reexamines this issue by exploiting a natural parametrization of a general Hankel matrix via its Vandermonde factorization. Such a formulation makes it possible to recast the notion of singular spectrum analysis as a semi-linear least squares problem over a compact feasible set, whence global optimization techniques can be employed to find the absolute best approximation. This framework might not be immediately suitable for practical application because global optimization is expectedly more expensive, but it does provide a theoretical baseline for comparison. As such, our empirical results indicate that the simpler SSA algorithm usually is amazingly sufficient as a handy tool for constructing exploratory model. The more complicated global methods could be used as an alternative of rigorous affirmative procedure for verifying or assessing the quality of approximation.
PubDate: 2014-11-01

• PubDate: 2014-11-01

• Ya. D. Sergeyev, R. G. Strongin and D. Lera: Introduction to global
optimization exploiting space-filling curves
• PubDate: 2014-11-01

• Branch-and-Sandwich: a deterministic global optimization algorithm for
optimistic bilevel programming problems. Part I: Theoretical development
• Abstract: We present a global optimization algorithm, Branch-and-Sandwich, for optimistic bilevel programming problems that satisfy a regularity condition in the inner problem. The functions involved are assumed to be nonconvex and twice continuously differentiable. The proposed approach can be interpreted as the exploration of two solution spaces (corresponding to the inner and the outer problems) using a single branch-and-bound tree. A novel branching scheme is developed such that classical branch-and-bound is applied to both spaces without violating the hierarchy in the decisions and the requirement for (global) optimality in the inner problem. To achieve this, the well-known features of branch-and-bound algorithms are customized appropriately. For instance, two pairs of lower and upper bounds are computed: one for the outer optimal objective value and the other for the inner value function. The proposed bounding problems do not grow in size during the algorithm and are obtained from the corresponding problems at the parent node.
PubDate: 2014-11-01

• Optimal curvature and gradient-constrained directional cost paths in
3-space
• Abstract: In the design of underground tunnel layout, the development cost is often dependent on the direction of the tunnel at each point due to directional ground fracturing. This paper considers the problem of finding a minimum cost curvature-constrained path between two directed points in 3-space, where the cost at every point along the path depends on the instantaneous direction. This anisotropic behaviour of the cost models the development cost of a tunnel in ground with faulting planes that are almost vertical. The main result we prove in this paper is that there exists an optimal path of the form $$\mathcal {C}\mathcal {S}\mathcal {C}\mathcal {S}\mathcal {C}\mathcal {S}\mathcal {C}$$ (or a degeneracy), where $$\mathcal {C}$$ represents a segment of a helix with unit radius and $$\mathcal {S}$$ represents a straight line segment. This generalises a previous result that in the restriction of the problem to the horizontal plane there always exists a path of the form $$\mathcal {C}\mathcal {S}\mathcal {C}\mathcal {S}\mathcal {C}$$ or a degeneracy which is optimal. We also prove some key structural results which are necessary for creating an algorithm which can construct an optimal path between a given pair of directed points in 3-space with a prescribed directional cost function.
PubDate: 2014-10-15

• Global solutions to nonconvex optimization of 4th-order polynomial and
log-sum-exp functions
• Abstract: Abstract This paper presents a canonical dual approach for solving a nonconvex global optimization problem governed by a sum of 4th-order polynomial and a log-sum-exp function. Such a problem arises extensively in engineering and sciences. Based on the canonical duality–triality theory, this nonconvex problem is transformed to an equivalent dual problem, which can be solved easily under certain conditions. We proved that both global minimizer and the biggest local extrema of the primal problem can be obtained analytically from the canonical dual solutions. As two special cases, a quartic polynomial minimization and a minimax problem are discussed. Existence conditions are derived, which can be used to classify easy and relative hard instances. Applications are illustrated by several nonconvex and nonsmooth examples.
PubDate: 2014-10-10

• Smoothing augmented Lagrangian method for nonsmooth constrained
optimization problems
• Abstract: Abstract In this paper, we propose a smoothing augmented Lagrangian method for finding a stationary point of a nonsmooth and nonconvex optimization problem. We show that any accumulation point of the iteration sequence generated by the algorithm is a stationary point provided that the penalty parameters are bounded. Furthermore, we show that a weak version of the generalized Mangasarian Fromovitz constraint qualification (GMFCQ) at the accumulation point is a sufficient condition for the boundedness of the penalty parameters. Since the weak GMFCQ may be strictly weaker than the GMFCQ, our algorithm is applicable for an optimization problem for which the GMFCQ does not hold. Numerical experiments show that the algorithm is efficient for finding stationary points of general nonsmooth and nonconvex optimization problems, including the bilevel program which will never satisfy the GMFCQ.
PubDate: 2014-10-08

• On the integration of row and column uncertainty in robust linear
programming
• Abstract: The usual formulation of a linear program is max $$c\cdot x{:}Ax \le b, x \ge 0$$ . The core part of this linear program is the $$A$$ matrix since the columns define the variables and the rows define the constraints. The $$A$$ matrix is constructed by populating columns or populating rows, or some of both, depending on the nature of the data and how it is collected. This paper addresses the construction of the $$A$$ matrix and solution procedures when there are separate data sources for the columns and for the rows and, moreover, the data is uncertain. The $$A$$ matrices which are “realizable” are only those which are considered possible from both sources. These realizable matrices then form an uncertainty set $$U$$ for a robust linear program. We show how to formulate and solve linear programs which provide lower and upper bounds to the robust linear program defined by $$U$$ . We also show how to use ordinary linear programming duality to share and divide the “credit/responsibility” of the optimal value of the robust linear program between the two alternative data sources.
PubDate: 2014-10-08

• Global optimization workshop 2012
• PubDate: 2014-10-01

• An integer linear programming formulation and heuristics for the minmax
relative regret robust shortest path problem
• Abstract: Abstract The well-known Shortest Path problem (SP) consists in finding a shortest path from a source to a destination such that the total cost is minimized. The SP models practical and theoretical problems. However, several shortest path applications rely on uncertain data. The Robust Shortest Path problem (RSP) is a generalization of SP. In the former, the cost of each arc is defined by an interval of possible values for the arc cost. The objective is to minimize the maximum relative regret of the path from the source to the destination. This problem is known as the minmax relative regret RSP and it is NP-Hard. We propose a mixed integer linear programming formulation for this problem. The CPLEX branch-and-bound algorithm based on this formulation is able to find optimal solutions for all instances with 100 nodes, and has an average gap of 17 % on the instances with up to 1,500 nodes. We also develop heuristics with emphasis on providing efficient and scalable methods for solving large instances for the minmax relative regret RSP, based on Pilot method and random-key genetic algorithms. To the best of our knowledge, this is the first work to propose a linear formulation, an exact algorithm and metaheuristics for the minmax relative regret RSP.
PubDate: 2014-10-01

• Finding multiple roots of a box-constrained system of nonlinear equations
with a biased random-key genetic algorithm
• Abstract: Abstract Several numerical methods for solving nonlinear systems of equations assume that derivative information is available. Furthermore, these approaches usually do not consider the problem of finding all solutions to a nonlinear system. Rather, most methods output a single solution. In this paper, we address the problem of finding all roots of a system of equations. Our method makes use of a biased random-key genetic algorithm (BRKGA). Given a nonlinear system, we construct a corresponding optimization problem, which we solve multiple times, making use of a BRKGA, with areas of repulsion around roots that have already been found. The heuristic makes no use of derivative information. We illustrate the approach on seven nonlinear equations systems with multiple roots from the literature.
PubDate: 2014-10-01

• Modeling and solving the bi-objective minimum diameter-cost spanning tree
problem
• Abstract: Abstract The bi-objective minimum diameter-cost spanning tree problem (bi-MDCST) seeks spanning trees with minimum total cost and minimum diameter. The bi-objective version generalizes the well-known bounded diameter minimum spanning tree problem. The bi-MDCST is a NP-hard problem and models several practical applications in transportation and network design. We propose a bi-objective multiflow formulation for the problem and effective multi-objective metaheuristics: a multi-objective evolutionary algorithm and a fast nondominated sorting genetic algorithm. Some guidelines on how to optimize the problem whenever a priority order can be established between the two objectives are provided. In addition, we present bi-MDCST polynomial cases and theoretical bounds on the search space. Results are reported for four representative test sets.
PubDate: 2014-10-01

• A new hybrid classical-quantum algorithm for continuous global
optimization problems
• Abstract: Abstract Grover’s algorithm can be employed in global optimization methods providing, in some cases, a quadratic speedup over classical algorithms. This paper describes a new method for continuous global optimization problems that uses a classical algorithm for finding a local minimum and Grover’s algorithm to escape from this local minimum. Such algorithms will be useful when quantum computers of reasonable size are available. Simulations with testbed functions and comparisons with algorithms from the literature are presented.
PubDate: 2014-10-01

• Optimal rank-sparsity decomposition
• Abstract: We describe a branch-and-bound (b&b) method aimed at searching for an exact solution of the fundamental problem of decomposing a matrix into the sum of a sparse matrix and a low-rank matrix. Previous heuristic techniques employed convex and nonconvex optimization. We leverage and extend those ideas, within a spatial b&b framework, aimed at exact global optimization. Our work may serve to (i) gather evidence to assess the true quality of the previous heuristic techniques, and (ii) provide software to routinely calculate global optima or at least better solutions for moderate-sized instances coming from applications.
PubDate: 2014-10-01

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