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Topology optimization has seen increased interest with the rise of additive manufacturing (AM) as a fabrication method, because of its ability to exploit the geometric complexity that AM offers. However, AM still imposes some geometric restrictions on the design, most notably on minimum feature size, overhang angles, and enclosed voids. Enclosed voids are problematic because for many AM methods it is impossible to remove supports, unmelted powder or uncured liquid from them. This paper introduces a filter based on a cumulative sum flood fill algorithm to alleviate this issue in a flexible manner. This filter produces a density field where every enclosed void element is rendered solid. It successfully eliminates enclosed voids in both 2D and 3D problems, with low computational cost due to its geometric nature. In addition we demonstrate direct control over the location, amount, and size of powder removal features by varying boundary conditions for the filter, running additional flood fills, and adding morphology operators, respectively. • The cumulative sum flood fill approach is introduced in topology optimization. • The approach works as a filter and successfully eliminates enclosed voids. • Direct control can be achieved over the amount, location, and size of access channels. • Due to its geometric nature it adds little computational effort. [ABSTRACT FROM AUTHOR]

• This paper proposed a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. • The parametric algorithm accounted for diverse types of bridge members and produced BIM bridge models using shape information automatically extracted per parameter. • The parametrically generated bridge BIM model was converted into PCD, serving as deep learning train data for automatic segmentation of bridge members. • The trained deep learning model showed high accuracy for segmenting superstructures, indicating promise for automated segmentation of bridge members. • The segmentation accuracy increased when accounting for the point density of members corresponding to the different sizes of bridge members. This paper proposes a comprehensive framework for parametric semi-automatic BIM and bridge member segmentation. For this, a parametric algorithm was developed to accommodate diverse superstructures, piers, abutments, and bearings. Each structural member in a bridge was classified to designate its shape information as parameters. The range of each parameter was defined to account for various dimensions of the shape of the bridge member. Therefore, libraries for each member were established to produce BIM bridge models using shape information automatically extracted per parameter. Then, the framework includes an algorithm that is developed to convert bridge BIM models generated by the parametric algorithm into PCD data. The virtually generated PCD is used as deep learning train data for automatic member segmentation based on the Point-Net algorithm. The trained algorithm using the virtual PCD was applied to a real bridge PCD. The segmentation of the bridge members showed high accuracy for the superstructure but low accuracy for the bearing. The segmentation accuracy of the algorithm including the bearing member could increase by modifying the density of PCD to account for the different sizes of bridge members. Furthermore, the proposed framework was applied to a UK bridge PCD and showed its applicability for use in bridges in different countries. [ABSTRACT FROM AUTHOR]

• Peoples who're specially affected with heart sickness and it's far one of the man-kill illnesses in the international level. • Most of researchers to awareness at the prediction, clustering, rule generation, decision tree and machine learning algorithm for figuring out and predicting the danger of the sufferers primarily based on the medical information. • The researcher can pick out the time and reminiscence wanted for the execution. • There are many different types of machine learning algorithms are categorized into three important classifications namely unsupervised learning, supervised learning and reinforcement learning. • This research paper includes the six elements to evaluate the overall performance of K-Means, Navie-Bayes and enhanced PSNB-IMR Algorithm with various parameters. Peoples who're specially affected with heart sickness and it's far one of the man-kill illnesses in the international level. Most of researchers to awareness at the prediction, clustering, rule generation, decision tree and machine learning algorithm for figuring out and predicting the danger of the sufferers primarily based on the medical information. The overall performances of the crucial functions are based on the machine-learning concept. By studying the algorithm, the researcher can pick out the time and reminiscence wanted for the execution. As such, there are many different types of machine learning algorithms are categorized into three important classifications namely unsupervised learning, supervised learning and reinforcement learning. Unsupervised learning consists of all varieties of clustering algorithms at the same time as supervised learning algorithm consists of all of the category strategies. But the author is considered the two algorithms are Supervised and Unsupervised learning algorithm to examine the overall performance. This research paper includes the six elements to evaluate the overall performance of K-Means, Navie-Bayes and enhanced PSNB-IMR Algorithm with various parameters. [ABSTRACT FROM AUTHOR]

• This paper develops a data aggregation technique using optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally data aggregation. • At first, the WSN-based IoT nodes are simulated, which are subjected to multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to base station. • In base station, the data aggregation is done using proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. • Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in WSN-based IoT network. The Internet of Things (IoT) is emerging that acquired the interest of several researchers, as it inspires each object to offer services and data to users by collaborating with each other. The Wireless sensor network (WSN) is a major constituent of IoT in transmitting data. Various wireless sensor-based protocols are developed for path selection. This paper develops a data aggregation technique using an optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally, data aggregation. At first, the WSN-based IoT nodes are simulated, which are subjected to a multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to the base station. In the base station, the data aggregation is done using the proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using the Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in a WSN-based IoT network. The developed DECSA+HABLMS offered superior performance with minimal prediction error of 0.0147. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm for coupling cohesive crack modeling with non-stationary heat flow. Firstly, the nonlinear system of equation, based on global formulations, for such a computational model is derived. The nonlinearity here comes from nonlinear relations in the crack. The relations refer to cohesion forces and to heat flux which both depend of crack opening and additionally are dependent of temperature difference between both sides of the crack. In the paper the discontinuities of displacement field and temperature field are both approximated using XFEM. All the analysis concerning crack surface is performed using local coordinate systems for each integration point. The local coordinate system is two-dimensional for both 2D and 3D analysis. The paper is illustrated with non-stationary thermo-mechanical examples for a domain with propagating crack. [ABSTRACT FROM AUTHOR]

• Three powerful metaheuristic algorithms are developed for optimum seismic design of steel frames. • The optimization algorithms tested on three benchmark problems including large-scale spatial steel frames. • Hybrid learning based jaya algorithm is the best optimizer in terms of minimum structural weight and computational cost. • The performance of proposed algorithms is insensitive to randomly generated initial populations. In this paper, three powerful metaheuristic algorithms, modified teaching learning based optimization, modified jaya algorithm and hybrid learning based jaya algorithm are presented for optimized seismic design of planar/spatial steel frames. The hybrid learning based jaya algorithm combines the learning phase of teaching learning based optimization and jaya algorithm. The goal is to design steel frames of minimum weight under strength, interstory drift and geometric-size constraints. One planar and two spatial steel frames previously designed for minimum weight by different metaheuristic optimization methods are taken as design examples to test the proposed methods. The methods presented in this paper are compared with other referenced algorithms in terms of optimized weight and computational cost. It is found that the hybrid learning based jaya algorithm outperforms modified teaching learning based optimization, modified jaya algorithm and its other competitors in all test problems. [ABSTRACT FROM AUTHOR]

It is well-known that analysis of incomplete Cholesky and LU decompositions with a general dropping is very difficult and of limited applicability, see, for example, the results on modified decompositions (Dupont et al., 1968; Gustafsson, 1978; Bern et al., 2006) and later results based on similar concepts. This is true not only for the dropping based on magnitude of entries but it also applies to algorithms that use a prescribed sparsity pattern. This paper deals with dropping strategies for a class of AINV-type incomplete decompositions (Benzi et al., 1996) that are based on the generalized Gram–Schmidt process. Its behavior in finite precision arithmetic has been discussed in Rozložník et al. (2012). This analysis enables better understanding of the incomplete process, and the main goal of the paper is to propose a new adaptive dropping strategy and to illustrate its efficiency for problems in structural mechanics. In addition, we add a brief comparison with another approximate inverse preconditioning strategy that is based on different principles and used in engineering applications. [ABSTRACT FROM AUTHOR]

In order to solve discretized stationary linear or non linear diffusion and convection–diffusion problems, the present paper deals with a comparison between the performances of parallel iterative synchronous or asynchronous algorithms implemented on a Cloud computing architecture with several sizes of the algebraic systems to be solved. The parallel algorithms are briefly and intuitively presented and analysis of their behavior is recalled. In particular, criteria of convergence are recalled and estimates of convergence speed are given. Implementation on cloud architectures are briefly described. Finally, for each kind of problem, the performance of the target algorithms on cloud architectures are analyzed. • Cloud-Based Synchronous and Asynchronous Parallel Algorithms • Resolution by Parallel Relaxation Methods for Linear & Non-Linear Algebraic Systems • Two distinct applications are presented and solved by the studied method • Implementation of algorithms on cloud architecture is described • Synchronous & asynchronous parallel behavior on Cloud for the two target algorithms [ABSTRACT FROM AUTHOR]

Abstract: The design of engineering systems under probabilistic model uncertainty using stochastic simulation techniques is discussed in this paper. A recently developed algorithm, called Stochastic Subset Optimization (SSO), is adopted for solution of the associated optimization problem. SSO is based on formulation of an augmented stochastic design problem and relies on simulation of samples from an auxiliary probability density function to establish a sensitivity analysis for the design variables. This stochastic sampling requires repeated evaluation of system model response and corresponds to the computationally most intensive part of the algorithm. A Moving Least Squares (MLS) response surface approximation is introduced here for reduction of this computational burden. The paper focuses on the efficient integration of the MLS approximation within SSO. Specifically, adaptive selection of the MLS weights is proposed exploiting information directly available from SSO to quantify the importance of the uncertain model parameters and the design variables in affecting the system response. A measure based on relative information entropy concepts is introduced for this purpose and its efficient evaluation is examined in detail. The selection of support points for generating the response surface is also addressed, and a novel measure is finally introduced for evaluating the efficiency of the response surface approximation in terms of the stochastic sampling accuracy. [Copyright &y& Elsevier]

Highlights • Constraint handling using augmented Lagrangian method. • Water cycle algorithm (WCA) is inspired by water cycle process and explosion of landmines. • Incorporation of augmented Lagrangian method in WCA called WCA-ALM. • WCA-ALM offers significant convergence efficiency. Abstract In this paper, a study was carried out to investigate the application and impact of the Augmented Lagrange Method (ALM) on the recently proposed Water Cycle Algorithm (WCA). The WCA-ALM was compared with the original WCA for constrained optimization of some engineering benchmark problems. The performance of quadratic penalty method was also investigated in this research. The approach transforms a constrained problem into an unconstrained problem by imposing penalty on the objective function. The comparison results show significant performance in convergence as well as solution quality in some cases. Furthermore, a simulation was also carried out to examine how the method handles constraints regarding feasible and infeasible solutions. The results show ALM efficiently handles the constraints for the WCA algorithm resulting in improved convergence and/or solution quality. [ABSTRACT FROM AUTHOR]

• An automatic algorithm for generating polyhedral meshes was systematically developed. • Hexahedral elements near the given STL surface were categorized into polyhedral volume topologies based on the MC algorithm. • Topology ambiguities of the MC algorithm were effectively resolved to provide an appropriate mesh for complex geometry. • The effectiveness was demonstrated through numerical analysis using the smoothed finite element method. Hexahedral meshes that exhibit the superiority in terms of solution accuracy and convergence rate are preferred to other types of meshes in the finite element analysis. However, the construction of the hexahedral meshes for complex geometries is still considered troublesome due to their poor geometric adaptability. This paper presents an efficient grid-based scheme to automatically generate polyhedral meshes including the hexahedral elements, and thus to provide hexahedral-dominant meshes for three-dimensional geometry with complex shapes. On the basis of the marching cube algorithm with a background grid composed of a regular arrangement of cubes, surface topologies for the background cubes are defined to represent the three-dimensional boundaries of a given domain. Then, in order to generate a three-dimensional finite element mesh, the surface topologies of the marching cube algorithm are systematically expanded to polyhedral volume topologies. Meanwhile, a topology ambiguity problem inherent in the marching cube algorithm is effectively resolved to generate an appropriate polyhedral mesh even for an arbitrary complex geometry. Several examples including biostructure modeling demonstrate that the proposed mesh generation scheme can easily discretize complex three-dimensional domains with hexahedral-dominant meshes, which are composed of the polyhedral elements near the domain boundaries and the hexahedral elements that come from the background cubes inside the domains. Furthermore, to show the applicability and effectiveness of polyhedral meshes in the finite element analysis, some structural analyses are performed using the smoothed finite element method that can be straightforwardly adapted to polyhedral elements of arbitrary shape. [ABSTRACT FROM AUTHOR]

Abstract: Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy. [Copyright &y& Elsevier]

Abstract: Considered the commercialization and the virtualization characteristics of cloud computing, the paper proposed for the first time an algorithm of job scheduling based on Berger model. In the job scheduling process, the algorithm establishes dual fairness constraint. The first constraint is to classify user tasks by QoS preferences, and establish the general expectation function in accordance with the classification of tasks to restrain the fairness of the resources in selection process. The second constraint is to define resource fairness justice function to judge the fairness of the resources allocation. We have expanded simulation platform CloudSim, and have implemented the job scheduling algorithm proposed in this paper. The experimental results show that the algorithm can effectively execute the user tasks and manifests better fairness. [Copyright &y& Elsevier]

Abstract: This paper presents a parameter sensitivity study of the Nelder–Mead Simplex Method for unconstrained optimization. Nelder–Mead Simplex Method is very easy to implement in practice, because it does not require gradient computation; however, it is very sensitive to the choice of initial points selected. Fan–Zahara conducted a sensitivity study using a select set of test cases and suggested the best values for the parameters based on the highest percentage rate of successful minimization. Begambre–Laier used a strategy to control the Particle Swarm Optimization parameters based on the Nelder Mead Simplex Method in identifying structural damage. The main purpose of the paper is to extend their parameter sensitivity study to better understand the parameter’s behavior. The comprehensive parameter sensitivity study was conducted on seven test functions: B2, Beale, Booth, Wood, Rastrigin, Rosenbrock and Sphere Functions to search for common patterns and relationships each parameter has in producing the optimum solution. The results show important relations of the Nelder–Mead Simplex parameters: reflection, expansion, contraction, and Simplex size and how they impact the optimum solutions. This study is crucial, because better understanding of the parameters behavior can motivate current and future research using Nelder–Mead Simplex in creating an intelligent algorithm, which can be more effective, efficient, and save computational time. [Copyright &y& Elsevier]

Abstract: This paper aims at the automatic design and cost minimization of reinforced concrete vaults used in road construction. This paper presents three heuristic optimization methods: the multi-start global best descent local search (MGB), the meta-simulated annealing (SA) and the meta-threshold acceptance (TA). Penalty functions are used for unfeasible solutions. The structure is defined by 49 discrete design variables and the objective function is the cost of the structure. All methods are applied to a vault of 12.40m of horizontal free span, 3.00m of vertical height of the lateral walls and 1.00m of earth cover. This paper presents two original moves of neighborhood search and an algorithm for the calibration of SA-TA algorithms. The MGB algorithm appears to be more efficient than the SA and the TA algorithms in terms of mean results. However, the SA outperforms MGB and TA in terms of best results. The optimization method indicates savings of about 10% with respect to a traditional design. [Copyright &y& Elsevier]

Abstract: This article creates a new method for the numerical integration of forced and damped oscillators, and their computational implementation. It also provides a generalisation of methods based on G-function and φ-function series. The algorithm produced in this paper integrates the non-perturbed problem with no truncation error, in which the perturbation parameter is a factor in the local truncation error. Under certain hypotheses, the new method calculates the exact solution of the perturbed problem as a series of τ-functions, the coefficients of which are obtained using simple algebraic recurrences involving the perturbation function. The new τ-function series method makes it possible to provide general solutions for certain problems in physics and engineering that are modelled using forced and damped oscillators. The method is more accurate than the well-known LSODE, MGEAR and GEAR methods in the way it resolves stiff and highly oscillatory problems, as the applications in this paper demonstrate. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the various solutions to the class of nonlocal boundary value problem with the help of finite element method for the class of Kirchhoff-type problemwhere is a bounded smooth domain, is continuous and has subcritical growth. Kirchhoff equation does not appear to have been previously studied in detail computationally and it is hope that this paper will help to provide a new idea in this direction. Here we solved the nonlinear function with the help of Newton Raphson method and quasi-linear term by using Scaling Iterative Algorithm, forward difference and with the help of these schemes; we used finite element method to solve the Quasilinear Elliptic Equation of Kirchhoff Type and discussed the behaviour. In order to numerically confirm our theoretical results and to demonstrate the performance of the algorithm, we develop a MATLAB program for solving Kirchhoff-type problem. [Copyright &y& Elsevier]

Abstract: This paper presents a global optimization approach for three layers of grid stack. In grid environment, the Quality of Service (QoS) needs to be supported at every layer of the grid architecture. However, less attention has been paid to incorporating QoS at multiple layers of the grid architecture. The primary objective of most existing scheduling mechanisms is to improve QoS at single grid layer, while the QoS optimization at multiple grid layers is seldom considered. In the paper, we consider the requirements of fabric layer, collective layer and application layer at the same time, and propose a global optimization approach for three layers of grid stack. The paper deals with global optimization as optimization decomposition. The global optimization for three layers of grid stack can be decomposed into three sub-problems at different grid layers: resource allocation at the fabric layer, service integration at the collective layer, and application QoS optimization problem at the application layer. A distributed iterative algorithm for three-layer optimization is proposed. The convergence of the iterative algorithm is proved. The simulations are conducted to test Three-Layer Optimization Algorithm. [Copyright &y& Elsevier]

Abstract: The perturbed harmonic oscillator appear frequently in the mathematical modelling of many problems in physics and engineering. The harmonic oscillator has a special purpose in Astrodynamics, because the Kunstaanheimo–Stiefel (KS) and Burdet–Ferrándiz (BF) transformations reduce the Kepler problem to harmonic oscillators. A new multi-step methods of numerical integration are introduced that generalize SMF ones. They are defined for arbitrary order and have similar properties to the former methods. Modified methods allowing step variations, whose coefficients are computed from relations of recurrence, are derived, what considerably improve the implementation of the algorithms. These methods are based in a sequence of analytical -functions dependent on two parameters and that generalizes the Scheifele’s G-functions and that, under wide hypothesis, allow us obtain the solution of harmonic oscillator. In this paper a new methodology is generated to solve the problem which the -functions series create regarding the calculus of recurrence relations transforming the method into a multistep scheme. The methodology is implemented in a computational algorithm, which lets us solve in a general way the problems of physics and engineering which are modalized by means of the study of the harmonic oscillators. Numerical examples already used by other authors are presented. They show how the new developed methods in this paper may compete in accuracy or efficiency with other well-reputed algorithms. [Copyright &y& Elsevier]

Abstract: One of the most important stages in the establishment of a cellular manufacturing system is the formation of manufacturing cells in order to find out which machines dedicated to each cell and part families corresponding to these machines. In this paper, two kinds of cells are being considered: (1) general or common cells which are able to manufacture different kinds of products and (2) specific cells which are able to manufacture a specific type of product. To set up cells for manufacturing, two kinds of capital constraints are observed: (1) capital constraints for construction and formation of cells and (2) capital availability constraints for the provision of tools and equipment to manufacture corresponding commodities. To find cells and to specify the family of the assigned commodities to each cell, different and various criteria exist. In this paper, three criteria are taken into consideration simultaneously in order to minimize the sum of: (1) costs of the delay in delivering a product to costumers by the above two cells in each period, (2) costs of the common and specific cells to remain idle in each period and (3) the unused capital. Since the cell formation problem is mostly time consuming, i.e. these are NP-hard, then to solve the problem, an effective algorithm of simulated annealing (SA) method is utilized. To verify and validate the efficiency of the SA algorithm, from the standpoint of the quality of the solution obtained and time of calculations, the results obtained are compared with those of the Lingo 6 software. Results suggest that the SA algorithm have good ability of solving the problem, especially in the case of large-sized problems for which Lingo 6 cannot produce solutions. [Copyright &y& Elsevier]

Abstract: This paper presents the resolution of multiobjective optimization problems as a tool in engineering design. In the literature, the solutions of this problems are based on the Pareto frontier construction. Therefore, substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The normalized normal constraint is a recent contribution that generates a well-distributed Pareto frontier. Nevertheless, these methods are susceptible of improvement or modifications to obtain the same level of results more efficiently. This paper proposes a modification of the original normalized normal constraint method using a genetic algorithms in the optimization task. The results presented in this paper show a suitable behavior for the genetic algorithms method compared to classical Gauss–Newton optimization methods which are used by the original normalized normal constraint method. [Copyright &y& Elsevier]

Abstract: This paper presents an algorithm to improve the performance of a spatial operation called ‘dissolve’ widely used in Geographic Information System (GIS) through spatial database systems. In simple feature models (lacking of persistent topology) executing some common spatial operations requires a high amount of system resources. Such common operations occur for example in the ‘OpenGIS Simple Features for SQL’ protocol (SFS), a client-server interoperability standard defined by ‘The Open Geospatial Consortium, Inc.’ (OGC). The specific spatial operation studied in this paper is called ‘dissolve’. It is carried out using the union spatial operator defined by OGC) and consists of removing the boundaries between adjacent polygons. The proposed algorithm improves substantially the performance of this spatial operation and it needs between 100 and 1000 times less amount of resources. This way it enables the database server to carry out this spatial operation on huge datasets containing up to millions of geometries. To check and to validate this algorithm a new open source software package (PGAT) has been developed. [Copyright &y& Elsevier]

Traditional overrun handling approaches for real-time systems enforce some isolation property at the job or task level. This paper shows that by “relaxing” task isolation, it is possible to efficiently deal with overruns in soft real-time systems with highly variable task execution times and proposes Randomized Dropping (RD), a novel overrun handling mechanism. RD is able to bound task overruns in a probabilistic manner, thus providing “soft” task isolation. The paper shows how to combine RD with priority-driven and rate-based scheduling algorithms, and how to analyze the resulting system. Performance evaluation and comparison between simulation and analytical results are discussed. [Copyright &y& Elsevier]

Radial basis functions are a popular basis for interpolating scattered data during the image reconstruction process in graphic analysis. In this context, the solution of a linear system of equations represents the most time-consuming operation. In this paper an efficient preconditioning technique is proposed to solve these linear systems of equations. This algorithm consists of an iterative method which enforces at each iteration a projection of the residual onto a suitable subspace called coarse space. This constraint is ensured by solving an auxiliary problem at each iteration without regularisation. As increasing the number of the coarse space basis functions increases the computational cost of the algorithm, correct selection of coarse space basis is addressed in the paper. Numerical results illustrate the convergence properties of the proposed method with wavelet-like basis functions and regular distributed radial basis functions for image reconstruction. [Copyright &y& Elsevier]

The paper presents the implementation of an association rules discovery data mining task using Grid technologies. For the mining task we are using the Apriori algorithm on top of the Globus toolkit. The case study presents the design and integration of the data mining algorithm with the Globus services. The paper compares the Grid version with related work in the field and we outline the conclusions and future work. [Copyright &y& Elsevier]

Abstract: This paper presents parallel computational strategies to implement explicit nonlinear finite element analysis code onto distributed memory parallel computers for solving large-scale problems in structural dynamics. Implementation details on both homogeneous and heterogeneous parallel processing environments are considered in detail in this paper. Implementation of an explicit nonlinear finite element dynamic analysis code on homogeneous systems is discussed first and this is later moved onto heterogeneous systems. Domain decomposition with explicit message passing is preferred for parallel implementation. The message passing implementation in the parallel algorithm is based on MPI (Message Passing Interface) libraries. Implementation aspects of overlapped, non-overlapped domain decomposition techniques, Dynamic Task Allocation (DTA) and clustering techniques for DTA and their relative merits are presented. The interprocessor communications are optimised by overlapping with computations to improve the performance of the domain decomposition based explicit dynamic analysis finite element code. The issues related to implementation of finite element code for nonlinear dynamic analysis on heterogeneous parallel computing environment are later presented. A new dynamic load-balancing algorithm is developed for this purpose and it is integrated with the domain decomposition based parallel explicit finite element code to test our algorithms on a coarse grain heterogeneous cluster of workstations. Numerical experiments have been carried out on PARAM-10000, an Indian parallel computer and also on cluster of Unix workstations. [Copyright &y& Elsevier]

Abstract: Programmable logic devices (PLDs) continue to grow in size and currently contain several millions of gates. At the same time, research effort is going into higher-level hardware synthesis methodologies for reconfigurable computing that can exploit PLD technology. In this paper, we explore the effectiveness and extend one such formal methodology in the design of massively parallel algorithms. We take a step-wise refinement approach to the development of correct reconfigurable hardware circuits from formal specifications. A functional programming notation is used for specifying algorithms and for reasoning about them. The specifications are realised through the use of a combination of function decomposition strategies, data refinement techniques, and off-the-shelf refinements based upon higher-order functions. The off-the-shelf refinements are inspired by the operators of communicating sequential processes (CSP) and map easily to programs in Handel-C (a hardware description language). The Handel-C descriptions are directly compiled into reconfigurable hardware. The practical realisation of this methodology is evidenced by a case studying the matrix multiplication algorithm as it is relatively simple and well known. In this paper, we obtain several hardware implementations with different performance characteristics by applying different refinements to the algorithm. The developed designs are compiled and tested under Celoxica’s RC-1000 reconfigurable computer with its 2 million gates Virtex-E FPGA. Performance analysis and evaluation of these implementations are included. [Copyright &y& Elsevier]

Abstract: In this paper, a methodology with both efficiency and effectiveness of using improved tabu search approach is applied successfully to solve one-dimensional cutting stock problem (1D-CSP). By introducing a varying mixed objective function, the chance of obtaining optimum solution in solving 1D-CSP, which is treated as a sequence problem, has increased. The total incentive trim loss, which functions as an incentive term in this paper, makes it possible to obtain good 1D-CSP results through the use of the new proposed TS approach. After testing against actual sample cases from shipyards and literatures, the solutions obtained from the proposed algorithm are superior. Computational results indicate that the methodology proposed outperforms the ones from other meta-heuristic methods. [Copyright &y& Elsevier]

Abstract: In the structural dynamics area, system identification represents one of the most critical steps. Therefore, a great research effort has been made in the last decades to improve the accuracy and reliability of this process. One of the results of this effort is the so-called stabilization diagram, a widespread tool, where the system''s poles are represented for several mod el orders. In real applications, the assessment of this diagram is often extremely demanding due to the high number of poles amongst which only a few represent the true or physical ones. Fuzzy clustering was already successfully applied in various fields including economics, finance and marketing. In this paper, fuzzy clustering is introduced into the structural mechanics field as a tool to automatically assess stabilization diagrams. Several advanced algorithms are presented, all based on the Fuzzy-C-Means clustering technique, including the Gustafson–Kessel and Gath–Geva algorithms. In addition, Genetic Algorithms can also be used to cluster a data set as a stand-alone technique as well as in a hybrid combination with fuzzy clustering algorithms. This paper concludes with a comparison of all the mentioned approaches, by applying them on true in-flight test data. [Copyright &y& Elsevier]

Abstract: The paper addresses a random search optimization method for nonlinear problems with continuous variables. The approach, called LJ-MM algorithm, deals with both unconstrained and constrained optimization problems. The algorithm was developed on the basis of the so called Luus–Jaakola (LJ) one, which was successfully used by several researchers to solve chemical and process engineering problems. The LJ-MM approach is aimed at highly multi-modal problems with sharp peaks. The major change in comparison with the LJ algorithm consists in different scheme of search space reduction rate. The tests carried out for several unconstrained and constrained problems proved its high performance for multi-modal problems with sharp peaks in particular. Also, they showed that it is the robust solver even in cases of problems with a smoother function. In all cases the performance of the LJ-MM approach depends only slightly on starting points and parameter setting. The detailed analysis of the test results and the comparison with the original LJ algorithm and others stochastic solvers is given in the paper. [Copyright &y& Elsevier]

Abstract: This paper presents a utility-based market mechanism for dynamic resource allocation in computational grid. The proposed competitive market economy is described. It consists of three different types of agents: grid request agent, grid resource agents, grid task agents; and a grid market that regulates the prices based on the observed aggregate excess demand. In order to solve the problem of heterogeneous demand in the grid, grid users'' preferences are summarized by means of their utility functions. We consider budget and complete times as constraints of optimization problem. The objective of the optimization problem is to determine the amount of resources under different constraints to maximize the users'' utilities. This paper proves the existence of a solution of the optimization problem, it also specify an iterative algorithm that is used by the grid market to update the prices, and it leads to an allocation that is close to a solution of the optimization problem in a finite number of iterations. The experiments are made to study characteristics of the utility directed allocation algorithm under budget constraint and under completed time constraint in terms of completion time and resource allocation efficiency. [Copyright &y& Elsevier]

Abstract: This paper expands the use of network flows to the multidimensional case, in which network flows are associated with vectors, instead of the conventionally used scalar values. A method for solving a multidimensional max-flow problem is systematically developed, on the basis of the primal-dual algorithm. It is demonstrated that upon reduction to a one-dimensional case, the method is transformed to the known Ford and Fulkerson algorithm. This multidimensional flow network can be applied to a variety of engineering applications, as the variables underlying engineering systems frequently possess a vector form. In this paper, the multidimensional max-flow problem is shown to correspond to the problem of plastic analysis of trusses. [Copyright &y& Elsevier]

It is very important to develop effective strategies for process industry to implement feasible scheduling while the process bottlenecks work optimally. However, the bottlenecks adopted by the existed scheduling strategies are often partial bottlenecks, local bottlenecks, or even deceptive bottlenecks, which are basically not key constraints to achieve optimal objective, so that the corresponding scheduling strategies are not perfect, and the further improvement and evaluation can hardly be proposed. So, it is a valuable issue to analyze the bottlenecks both in academic and engineering fields. This paper aims at the minimum cost problem of generalized network flow model to define and analyse three classes of bottlenecks based on generalized network simplex algorithm, and the corresponding search algorithms are proposed in this paper. The obtained bottlenecks cannot only be used to determine whether the object cost will be increased or decreased, but also be used to propose the corresponding strategy to evaluate the improvement on the network flow model for scheduling. Finally, a typical example is discussed. [Copyright &y& Elsevier]

Evolutionary optimization algorithms are widely used for solving optimization problems. However, in the realm of real-world structures finding the optimum design would be difficult and time-consuming due to large number of design variables, enormous size of the search space, and availability of numerous constraints. As a result, holistic optimization approaches that consider architectural and other practical requirements in addition to required code provisions are rather limited in real-world structures. This paper presents an innovative algorithm combining multi-criterion decision-making (DM) and Particle Swarm Optimization (PSO), which is called DMPSO, for accelerating convergence toward optimum solution in 3D reinforced concrete (RC) frames. All practical requirements are considered for obtaining directly constructible designs without any further modifications. The effectiveness of the proposed algorithm is illustrated in optimization of 3D RC frames subjected to lateral seismic forces according to ASCE 7 requirements. The results confirm the ability of the proposed DMPSO algorithm to efficiently find optimal solutions for 3D RC optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, modified schemes are proposed for preventing a grey wolf optimizer (GWO) from premature exploration and convergence on optimization problems. Three novel strategies are developed to improve the performance of existing GWO. The first strategy uses the concept of prey weight. The second strategy uses the astrophysics concepts, which guide the grey wolves toward more promising areas of the search space. The beauty of this strategy is to let each grey wolf learn from not only movement of sun (symbolizes prey) in the search space but also the wolves are made to explore and exploit simultaneously. Third strategy combines the both, first and second strategies to take advantages of prey weight and astrophysics strategies. The proposed improvements in GWO have been evaluated on thirteen benchmark test functions. The performance of the proposed modifications has been compared with other five recently developed state-of-the-art techniques. The effects of scalability, noise, and control parameter have also been investigated. The statistical tests have been performed to validate the significance of modified variants. The proposed variants are also applied for seven well-known constrained engineering design problems. The experimental results depict the supremacy of the proposed modified algorithm in solving engineering design problems when compared with several existing techniques. [ABSTRACT FROM AUTHOR]

Thermal stress analysis is one of key aspects in mechanical design. Based on the indirect boundary integral equation (BIE) and the radial integration method (RIM), this paper develops a boundary-only element method for the boundary stress analysis of three-dimensional (3D) static thermoelastic problems. A transformation system constructed with the normal and two special tangential vectors is used to regularize the singularity in the indirect BIE. The RIM is then employed to transform the domain integrals arising in both displacement and its derivative integral equations into the equivalent boundary integrals, which results in a pure boundary discretized algorithm. Several numerical experiments are provided to verify the accuracy and convergence of the present approach. [ABSTRACT FROM AUTHOR]

• Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm is presented. • Effects of Heaviside smooth and step functions on SEMDOT are investigated. • The benefits and distinctions of SEMDOT compared to well-established element-based algorithms are shown. • The source code of SEMDOT is provided. Element-based topology optimization algorithms capable of generating smooth boundaries have drawn serious attention given the significance of accurate boundary information in engineering applications. The basic framework of a new element-based continuum algorithm is proposed in this paper. This algorithm is based on a smooth-edged material distribution strategy that uses solid/void grid points assigned to each element. Named Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT), the algorithm uses elemental volume fractions which depend on the densities of grid points in the Finite Element Analysis (FEA) model rather than elemental densities. Several numerical examples are studied to demonstrate the application and effectiveness of SEMDOT. In these examples, SEMDOT proved to be capable of obtaining optimized topologies with smooth and clear boundaries showing better or comparable performance compared to other topology optimization methods. Through these examples, first, the advantages of using the Heaviside smooth function are discussed in comparison to the Heaviside step function. Then, the benefits of introducing multiple filtering steps in this algorithm are shown. Finally, comparisons are conducted to exhibit the differences between SEMDOT and some well-established element-based algorithms. The validation of the sensitivity analysis method adopted in SEMDOT is conducted using a typical compliant mechanism design case. In addition, this paper provides the Matlab code of SEMDOT for educational and academic purposes. [ABSTRACT FROM AUTHOR]

• The total-compliance-variation is introduced for the robust truss-sizing problem with directionally uncertain loads as a new robustness measure. • A one-step asymptotically optimal and a two-steps metaheuristic algorithm are developed for the weight-constrained-total-compliance-variation-minimization. • The one-step algorithm is working on the usual search space spanned by the design variables with two alternative objectives. • The two-steps algorithm firstly defines a lower dimensional search space, and then uses a hybrid metaheuristic search strategy. • The two algorithms are benchmarked on the classical ten-bar truss sizing problem with one, two, and four directionally uncertain loads. In this paper, the possible numerical treatments of a new compliance-function-shape-oriented robust truss sizing model with directionally uncertain loads have been analyzed. The applied robustness measure is the total-compliance-variation; the corresponding model is a total-compliance-variation minimization model. The model, according to the number of uncertain loads, can be classified as a curve-length, surface-area, volume, and generalized volume minimization problem where the objective is integral and the weight increase is constrained. The numerical treatment of the model is a challenging problem. In this paper, for the numerical treatment a one-step asymptotically optimal algorithm and a two-steps heuristic algorithm are presented. The central element of the one-step algorithm is a mesh with mesh-point-specific displacements, stress constraints, and compliance values. The algorithm can be formulated as a sample-based iterative process using one of the two alternative objectives. The one-step algorithm is computationally expensive but able to find an optimal solution with appropriate maximum running time and mesh-size settings. The two-steps algorithm firstly defines a lower dimensional searching space without displacements and stress constraints and uses it as the searching space in the second step. The hybrid metaheuristic search method is a modified ANGEL algorithm, which is a hybridization of the ant-colony (AN), the genetic (GE) and a local search (L) algorithms. The two-steps algorithm is faster but its result is not necessary optimal. To illustrate the efficiency of the solution possibilities the popular ten-bar truss sizing problem will be used with one, two, and four directionally uncertain point loads as the benchmark problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel low cost scheduling driven watermarking methodology for modern computer aided design (CAD) high level synthesis tools. The proposed watermarking algorithm is embedded in the scheduling module of a CAD high level synthesis (HLS) tool. The presented watermarking methodology is capable of reusable intellectual property (IP) core protection of control data flow graphs (CDFG) from a vendor's perspective based on user provided resource constraint and loop unrolling factor as inputs. The proposed low cost robust watermarking embedded inside high level synthesis process protects an IP core against threats such as false claim of ownership and piracy. The proposed watermarking satisfies desirable properties such as covertness, robustness, low embedding cost and low complexity. Results of comparison indicated significant reduction in embedding cost through proposed technique than similar state of the art techniques. [ABSTRACT FROM AUTHOR]

A stochastic multiscale computational method for predicting the thermo-mechanical coupling properties of random porous materials is proposed. Here, the thermal radiation effect at microscale of the material consisting of randomly distributed pores is considered, which has an important impact on the macroscopic temperature and stress fields. A novel unified homogenization procedure, based on two-scale asymptotic expressions, has been established. The higher-order multiscale formulations for solving the dynamic thermo-mechanical coupling problem with the inertia term, coupling term and radiation term are given successively. Then, the statistical prediction algorithm based on the multiscale method is described in detail. Finally, numerical examples for porous materials with varying probability distribution models are calculated by the proposed algorithm, and compared with the data by finite element method with very fine meshes. The comparison shows that the stochastic multiscale computational method developed in this paper is useful for determination of the thermo-mechanical properties of porous materials and demonstrates its potential applications in engineering and technology. [ABSTRACT FROM AUTHOR]

• Constructing a novel combined cubic generalized Ball (CCG-Ball, for short) surfaces. • Establishing the shape optimization model of CCG-Ball surfaces. • Solving the established model based on an enhanced jellyfish search (EJS) algorithm. • Comparing EJS algorithm with state-of-the-art algorithms for shape optimization. • EJS algorithm is competitive and superior to comparative algorithms. The construction and shape optimization of complex shape-adjustable surfaces is a crucial and intractable technique in Computer Aided geometric Design (CAGD), which has a wide range of application value in many product designs and manufacturing fields involving complex surfaces modeling. In this paper, a novel combined cubic generalized Ball (CCG-Ball, for short) surfaces is constructed and the shape optimization of CCG-Ball surfaces is studied by an enhanced jellyfish search (JS) algorithm. First and foremost, we construct the CCG-Ball surfaces with multiple shape parameters based on a class of cubic generalized Ball basis functions, and then derive the conditions of G1 and G2 continuity for the surfaces. The shapes of CCG-Ball surfaces can be adjusted and optimized expediently by utilizing their shape parameters. Secondly, the shape optimization of CCG-Ball surfaces is mathematically an optimization problem that can be efficiently dealt with by swarm intelligence algorithm. In this regard, an enhanced JS termed EJS algorithm, combined with sine and cosine learning factors, local escape operator, opposition-based learning and quasi-opposition learning strategies, is introduced to improve the convergence speed and calculation accuracy of the JS algorithm. Finally, by minimizing the energy of CCG-Ball surfaces as the evaluation standard, the shape optimization models of the surfaces with G1 and G2 geometric continuity are established, respectively. The EJS algorithm is utilized to solve the established models, and the CCG-Ball surfaces with minimum energy are obtained. The example results illustrate the ability of EJS algorithm in effectively solving the shape optimization problems of complex CCG-Ball surfaces. [ABSTRACT FROM AUTHOR]

• A method of design for ball Said-Ball (BSB) curve is proposed. • The geometrical properties and algorithms of BSB curve are investigated. • The conditions for G1 and G2 continuity of BSB curves are obtained. • An extension method of BSB curve with minimum energy is presented. • A novel BSB rotation surface with thickness is constructed. The representation method of curves and surfaces is an essential tool in industrial design, while a ball curve with adjustable thickness has its advantages in representing freedom tubular objects. By combing the Said-Ball basic functions and the control balls, this paper constructs the ball Said-Ball (BSB, for short) curve and discusses the related geometrical properties and algorithms. Then, the G 1 and G 2 continuity conditions between two adjacent BSB curves are discussed. Furthermore, on the premise of G 2 continuity, an extension method of the BSB curve with minimum energy is studied. For this method, the GaussNewton-NL2SOL algorithm is used to obtain the best scale parameters of the center curve. Meanwhile, the optimal parameters of the radius function are also determined by solving the extreme. Finally, the construction of the BSB rotation surface with different continuity is given. The provided modeling examples show that the BSB curve with thickness is more suitable and valuable in the design for the modeling of objects, on the premise of maintaining excellent geometric properties. [ABSTRACT FROM AUTHOR]

• New point operators for picture fuzzy sets are defined. • Memoryless multiple rounds voting process is stated. • A multi-attribute decision making algorithm is presented to address the problems. • Superiority of the algorithm is demonstrated through numerical examples. Picture fuzzy set (PFS), a generalization of the Atanassov-intuitionistic fuzzy set, is one of the most trustworthy tool to handle the uncertainties in the data during the decision-making process. The objective of this paper is to present some different kinds of the point operators for the PFS. The key feature of the presented point operators is to provide a smaller degree of uncertainty which is equivalent to the corresponding PFS. The desirable properties of the proposed operators are also derived in details. Also, we provide a memoryless multiple rounds voting process under the PFS environment to strength the result, and hence deduce a relationship between the proposed PFS and the existing set. In addition to this, we address the MADM (multiple attribute decision-making) problems by presented an algorithm to solve the problem based on the proposed point operators. The applicability and the superiority of the proposed algorithm are shown through two numerical examples. At last, the validity of the proposed algorithm is tested using different test criteria. [ABSTRACT FROM AUTHOR]

• This paper aims to design a new text-to-image synthesis scheme. • Initially, throughout text to image encoding, cross modal feature grouping is done. • BI-LSTM is employed for converting the text embeddings into text feature vectors. • In the subsequent stage, the images are synthesized depending upon the encoding. • The GAN is trained with optimization strategy by tuning optimal weights using DC-WO. Text to image synthesizes is a motivating and valuable task to explore in these days. The objective is to produce images from texts or captions, i.e. considering textual descriptions as input and deploying them to produce related images. For carrying this task, two challenging elements of image generation and language modeling should be combined that are said to be more complex than caption generation. This research aims to develop a new text-to-image synthesis method that includes the two crucial steps of (i) text to picture encoding and (ii) optimized GAN. At first, cross-modal feature grouping is done throughout the text to image encoding. Consequently, text embeddings are transformed into textual feature vectors using BI-LSTM. The images are created based on the encoding in the following stage. Thus, optimized GAN receives input from text feature groups and outputs the end synthesis images. Here, a research plans to train the GAN with optimization strategy via tuning the optimal weights using the Dragon Customized Whale Optimization (DC-WO) model. Finally, the superiority of the created approach is evaluated by comparing it to existing techniques. Additionally, the FID value of the proposed model using the cub dataset is 29.04%, 47.43%, 49.09%, 37.64% and 11.35%, superior to traditional FF, WOA, GWO, DA and GAN + CMAF models. [ABSTRACT FROM AUTHOR]

One of the factors responsible for the mechanical strength of objects manufactured by thermoplastic extrusion 3D printers is the adhesion between adjacent material beads in the same layer. This, in turn, depends on the time lapsed between their deposition. In this paper we describe HotFill , an algorithm that finds a tool-path for solid raster infill that keeps the cooling time intervals between depositions below user-specified limits. The algorithm, based on the dynamic programming technique, was implemented in Python and tested on several representative slices of 3D models. The results show that HotFill is fast and produces tool-paths that have low fabrication time while respecting the cooling time constraints. The source code and datasets are available at GitHub. • Strength of a 3D printed part depends on good bonding between infill rasters. • Bond strength depends on time lapsed between deposition of adjacent rasters. • Problem: build a tool-path with prescribed limits for inter-raster cooling times. • A practical dynamic programming algorithm for time-constrained tool-path generation is proposed. • The algorithm is tested on some representative part slices. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a novel hybrid algorithm LSMA-TLBO for solving the numerical and engineering design optimization problems. The proposed algorithm utilizes the features of Slime Mould Algorithm (SMA) and the Teaching–Learning Based Optimization (TLBO) to balance the exploitation and exploration ability. In addition, a lévy flight based mutation is introduced in this study to maximize the exploration ability of the algorithm. SMA is one of the meta-heuristic algorithms (MHAs) which mimics the oscillation mechanism of slime mould in nature. It is an efficient technique that has been successfully applied to the complex optimization problems (OPs) due to its global exploration ability. However, despite its popularity, the SMA also suffers from some shortcomings, such as local optima stagnation, slow convergence rate and improper balance between exploitation and exploration. In view of this weaknesses, the hybrid algorithm LSMA-TLBO is proposed considering the good global search ability of SMA, fast convergence of TLBO and the features of the lévy flight based mutation to maximize the exploration ability. The performance of the proposed LSMA-TLBO is evaluated on 23 different benchmark functions including uni-modal and multi-modal problems. In addition, IEEE CEC-C06 2019 test suite and six engineering design problems are also considered to examine the practicable abilities of the proposed method. The results are compared with several recently proposed state-of-the-art algorithms and the simulation results show that the proposed method outperforms other algorithms. Further, the LSMA-TLBO is executed through statistical testing of hypothesis namely Wilcoxon's rank-sum test. It is concluded that the proposed LSMA-TLBO is more competitive and effective algorithm to solve real-world complex OPs. • A novel hybrid algorithm LSMA-TLBO is proposed for optimization problems. • Levy flight-based mutation operator is introduced to increase the exploitation capability. • Benchmarks CEC-C06-2019 and Engineering design problems have been taken to validate the approach. • Solutions by the proposed approach are compared with the several state-of-the-art. • Statistical test such as Wilcoxon's-rank sum test, box plot, are performed to rank the algorithms. [ABSTRACT FROM AUTHOR]

• A novel method to improve the performance of population-based optimizers. • The combination lock algorithm used as a pre-processing optimization method. • The combination lock algorithm produces an elite individual. • The elite individual is inserted into the initial population of optimizers. • The new algorithm significantly improves the performance of optimizers. Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems. In this paper, a novel systematic pre-processing approach, called the combination lock algorithm, for obtaining a good starting point for population-based algorithms is proposed. The proposed algorithm is tested on 32 benchmark unconstrained multidimensional optimization problems of different characteristics that are either unimodal or multimodal, continuous or non-continuous, separable or non-separable, differentiable or non-differentiable. The tests also include four engineering constrained optimization benchmark problems. The experimental results of applying the proposed algorithm for the Particle Swarm Optimization, the Ant Colony Optimization for Continuous Domain, and the Grey Wolf Optimization were compared with the results obtained from the conventional approach of initializing the starting population of population-based metaheuristic methods. The simulation results show the potential of the proposed algorithm as an efficient and reliable approach to enhance the performance of population-based optimization algorithms such that, overall, 50% and up to 100% of the tested problems "across various population size" settings, had either improved or equalled the optimal values when the proposed algorithm was applied. [ABSTRACT FROM AUTHOR]

The Colliding Bodies Optimization (CBO) algorithm is a metaheuristic algorithm inspired by the physics laws of collision in which each candidate solution is modeled as an agent with mass body in proportion to the fitness of the solution. In this paper a modified version of CBO, denoted by MCBO, is utilized to optimize the cost of bridge superstructures. The problem consists of 17 variables and 101 implicit constraints based on AASHTO standard specifications and construction limitations. The optimization is performed for bridges with different span lengths and deck widths, and with various unit costs of concrete. A comparison among the PSO, CBO, and MCBO algorithms is conducted which shows the efficiency and robustness of the MCBO algorithm. [ABSTRACT FROM AUTHOR]

Convex hulls are fundamental geometric tools used in a number of algorithms. This paper presents a fast, simple to implement and robust Smart Convex Hull (S-CH) algorithm for computing the convex hull of a set of points in E 3 . This algorithm is based on “spherical” space subdivision. The main idea of the S-CH algorithm is to eliminate as many input points as possible before the convex hull construction. The experimental results show that only a very small number of points are used for the final convex hull calculation. Experiments made also proved that the proposed S-CH algorithm achieves a better time complexity in comparison with other algorithms in E 3 . [ABSTRACT FROM AUTHOR]

This paper introduces a mesh generation strategy devised and implemented for the volume element model (VEM), and elaborates key contributions of the strategy in enhancing the VEM as a prominent tool in ship thermal modeling and simulation. The VEM mesh generation strategy employs ray crossings and ray– triangle intersection algorithms developed in previous studies, and constructs sufficiently accurate geometric representations of the whole ship within permissible time frame using hexahedral meshes. In addition, this work demonstrates the strategy’s practicality in thermal analysis of a notional all-electric ship, which is characterized by intricate structures and multiple internal components, i.e., thermal loads. Ship thermal solutions obtained in this assessment verify the proposed mesh generation strategy’s ability to improve the overall computational efficiency of the VEM, by allowing it to obtain plausible thermal solutions with respect to time and space using a coarse independent mesh. [ABSTRACT FROM AUTHOR]