Showing papers in "Engineering Optimization in 2018"
TL;DR: The method first uses the ‘locating the regional extreme’ criterion, which incorporates minimizing the surrogate model while also maximizing the expected improvement criterion, and replaces the Kriging models by the KPLS(+K) models, which are more suitable for high-dimensional problems.
Abstract: In many engineering optimization problems, the number of function evaluations is often very limited because of the computational cost to run one high-fidelity numerical simulation. Using a classic optimization algorithm, such as a derivative-based algorithm or an evolutionary algorithm, directly on a computational model is not suitable in this case. A common approach to addressing this challenge is to use black-box surrogate modelling techniques. The most popular surrogate-based optimization algorithm is the efficient global optimization (EGO) algorithm, which is an iterative sampling algorithm that adds one (or many) point(s) per iteration. This algorithm is often based on an infill sampling criterion, called expected improvement, which represents a trade-off between promising and uncertain areas. Many studies have shown the efficiency of EGO, particularly when the number of input variables is relatively low. However, its performance on high-dimensional problems is still poor since the Kriging models used are time-consuming to build. To deal with this issue, this article introduces a surrogate-based optimization method that is suited to high-dimensional problems. The method first uses the ‘locating the regional extreme’ criterion, which incorporates minimizing the surrogate model while also maximizing the expected improvement criterion. Then, it replaces the Kriging models by the KPLS(+K) models (Kriging combined with the partial least squares method), which are more suitable for high-dimensional problems. Finally, the proposed approach is validated by a comparison with alternative methods existing in the literature on some analytical functions and on 12-dimensional and 50-dimensional instances of the benchmark automotive problem ‘MOPTA08’.
85 citations
TL;DR: In this article, the thermal exchange optimization (TEO) algorithm was applied to a damage detection problem. But the authors defined the damage detection as an inverse problem and applied it to a wide range of structures, and several scenarios with noise and noise-free modal data were tested and the locations and extents of damages were identified with good accuracy.
Abstract: The recently developed optimization algorithm—the so-called thermal exchange optimization (TEO) algorithm—is enhanced and applied to a damage detection problem. An offline parameter tuning approach is utilized to set the internal parameters of the TEO, resulting in the enhanced heat transfer optimization (ETEO) algorithm. The damage detection problem is defined as an inverse problem, and ETEO is applied to a wide range of structures. Several scenarios with noise and noise-free modal data are tested and the locations and extents of damages are identified with good accuracy.
68 citations
TL;DR: The results reveal that the MDO algorithm can improve the rock fragmentation and increase the energy utilization rate of explosives in large-diameter longhole blasting operations and could be extremely helpful in the process of underground mine blast optimization.
Abstract: The charge structure is an important factor in the adjustment of the energy distribution of explosives and the control of the blasting effect, especially in an underground large-diameter longhole, ...
57 citations
TL;DR: Numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization.
Abstract: In spite of considerable research work on the development of efficient algorithms for discrete sizing optimization of steel truss structures, only a few studies have addressed non-algorithmic issues affecting the general performance of algorithms. For instance, an important question is whether starting the design optimization from a feasible solution is fruitful or not. This study is an attempt to investigate the effect of seeding the initial population with feasible solutions on the general performance of metaheuristic techniques. To this end, the sensitivity of recently proposed metaheuristic algorithms to the feasibility of initial candidate designs is evaluated through practical discrete sizing of real-size steel truss structures. The numerical experiments indicate that seeding the initial population with feasible solutions can improve the computational efficiency of metaheuristic structural optimization algorithms, especially in the early stages of the optimization. This paves the way for efficient m...
57 citations
TL;DR: An iterative search framework based on the proposed insertion algorithm for the single-objective scheduling problem of minimizing the total electricity cost for a given number of machines can find high-quality Pareto fronts for large-size problems with up to 5000 jobs.
Abstract: This article investigates a bi-objective scheduling problem on uniform parallel machines considering electricity cost under time-dependent or time-of-use electricity tariffs, where electricity price changes with the hours within a day. The aim is to minimize simultaneously the total electricity cost and the number of machines actually used. A bi-objective mixed-integer linear programming model is first formulated for the problem. An insertion algorithm is then proposed for the single-objective scheduling problem of minimizing the total electricity cost for a given number of machines. To obtain the whole Pareto front of the problem, an iterative search framework is developed based on the proposed insertion algorithm. Computational results on real-life and randomly generated instances demonstrate that the proposed approach is quite efficient and can find high-quality Pareto fronts for large-size problems with up to 5000 jobs.
49 citations
TL;DR: A novel predictor–corrector (PC) method for the numerical treatment of multi-objective optimization problems (MOPs) that is capable of performing a continuation along the set of (local) solutions of a given MOP with k objectives, and can cope with equality and box constraints.
Abstract: This article proposes a novel predictor–corrector (PC) method for the numerical treatment of multi-objective optimization problems (MOPs). The algorithm, Pareto Tracer (PT), is capable of performing a continuation along the set of (local) solutions of a given MOP with k objectives, and can cope with equality and box constraints. Additionally, the first steps towards a method that manages general inequality constraints are also introduced. The properties of PT are first discussed theoretically and later numerically on several examples.
48 citations
TL;DR: The present approach can achieve better performance in terms of computational efficiency and iteration number than the solid isotropic material with penalization method solved by the commonly used method of moving asymptotes.
Abstract: Transient heat conduction analysis involves extensive computational cost. It becomes more serious for multi-material topology optimization, in which many design variables are involved and hundreds of iterations are usually required for convergence. This article aims to provide an efficient quadratic approximation for multi-material topology optimization of transient heat conduction problems. Reciprocal-type variables, instead of relative densities, are introduced as design variables. The sequential quadratic programming approach with explicit Hessians can be utilized as the optimizer for the computationally demanding optimization problem, by setting up a sequence of quadratic programs, in which the thermal compliance and weight can be explicitly approximated by the first and second order Taylor series expansion in terms of design variables. Numerical examples show clearly that the present approach can achieve better performance in terms of computational efficiency and iteration number than the sol...
47 citations
TL;DR: The proposed algorithm, called fully stressed design–grey wolf–adaptive differential evolution (FSD-GWADE), is demonstrated to tackle a variety of truss optimization problems that have mixed continuous/discrete design variables.
Abstract: A hybrid adaptive optimization algorithm based on integrating grey wolf optimization into adaptive differential evolution with fully stressed design (FSD) local search is presented in this article....
47 citations
TL;DR: In this article, the storage assignment and order batching problem in the Kiva mobile fulfilment system is studied, where storage assignment aims to decide which product to put in which pod to maximize the product similarity and the orderbatching model aims to minimize the number of visits of pods.
Abstract: This article studies the storage assignment and order batching problem in the Kiva mobile fulfilment system. The storage assignment model aims to decide which product to put in which pod to maximize the product similarity and the order batching model aims to minimize the number of visits of pods. To solve the order batching problem, a heuristic is proposed, where a batch schedule is initialized with the objective of maximizing the order association or minimizing order alienation and improved by variable neighbourhood search. Computational experiments are conducted to verify the performance of the proposed model and algorithm.
45 citations
TL;DR: An improved fireworks algorithm (IFWA) is proposed to mitigate the drawbacks of the original algorithm, and the efficiency of the proposed IFWA is compared with other well-known metaheuristics in the literature, in terms of the optimum solutions and the convergence rate.
Abstract: The fireworks algorithm (FWA), a newly introduced metaheuristic algorithm, has exhibited remarkable performance in a variety of optimization problems. However, FWA still has some shortcomings that ...
39 citations
TL;DR: A hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is presented for global optimization of large-scale dome truss structures.
Abstract: In this article a hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is ...
TL;DR: A new method, based on the particle swarm optimization technique, which employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight, to handle highly-constrained multi-objective optimization problems.
Abstract: This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO...
TL;DR: The Iso-XFEM method is further developed to enable the topology optimization of geometrically nonlinear structures undergoing large deformations by implementing a total Lagrangian finite element formulation and defining a structural performance criterion appropriate for the objective function of the optimization problem.
Abstract: Iso-XFEM method is an evolutionary optimization method developed in our previous studies to enable the generation of high resolution topology optimised designs suitable for additive manufacture. Conventional approaches for topology optimization require additional post-processing after optimization to generate a manufacturable topology with clearly defined smooth boundaries. Iso-XFEM aims to eliminate this time-consuming post-processing stage by defining the boundaries using isovalues of a structural performance criterion and an extended finite element method (XFEM) scheme. In this paper, the Iso-XFEM method is further developed to enable the topology optimization of geometrically nonlinear structures undergoing large deformations. This is achieved by implementing a total Lagrangian finite element formulation and defining a structural performance criterion appropriate for the objective function of the optimization problem. The Iso-XFEM solutions for geometrically nonlinear test-cases implementing linear and nonlinear modelling are compared, and the suitability of nonlinear modelling for the topology optimization of geometrically nonlinear structures is investigated.
TL;DR: An adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model and shows that it provides a more accurate metamodel at the same simulation cost, which is very important in metAModel-based engineering design problems.
Abstract: Variable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of...
TL;DR: In this paper, the optimal lay-up design for the maximum fundamental frequency of variable stiffness laminated composite plates was investigated using a layer-wise optimization technique, where the design variables are
Abstract: The optimal lay-up design for the maximum fundamental frequency of variable stiffness laminated composite plates is investigated using a layer-wise optimization technique. The design variables are ...
TL;DR: This work considers the optimization of an H-Darrieus wind turbine using a genetic algorithm, owing to its robustness; the airfoil shape is varied to maximize the energy output at the optimal tip-speed ratio using detailed unsteady, turbulent, computational fluid dynamics simulations relying on the commercial tool StarCCM+.
Abstract: Wind energy is continuously growing in importance owing to increasingly strict environmental regulations. Although Horizontal Axis Wind Turbines (HAWTs) are the most popular, Vertical Axis Wind Tur...
TL;DR: A methodology which integrates single-objective evolutionary algorithms (EAs) and finite element (FE) model updating for damage inference in three-dimensional (3D) structures is presented.
Abstract: This study presents a methodology which integrates single-objective evolutionary algorithms (EAs) and finite element (FE) model updating for damage inference in three-dimensional (3D) structures. First, original well-known EAs, namely the genetic algorithm, differential evolution (DE) and particle swarm optimization (PSO), are combined with FE model updating for detecting damage in a 3D four-storey modular structure and their performances are compared. Next, to obtain more accurate results, hybrid Levy flights–DE and hybrid artificial bee colony–PSO are developed for enhancing damage identification. With each method, the objective function composed of modal strain energy and mode shape residuals, taken from the FE model of the intact structure and the simulated damage responses, is initially created. Then, the performance of each algorithm combined with FE model updating for damage detection is assessed in terms of three characteristics: consistency, computational cost and accuracy, and the best p...
TL;DR: It is proven that, given, if then the original problem converges to a unique solution with the minimal weighted norm, and the proposed projection-gradient method under mild conditions is established.
Abstract: This article deals with the Tikhonov regularization method for the constrained Lagrange approach, taking into account polylinear programming problems. A regularized Lagrange function is str...
TL;DR: In this article, the authors considered a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity, and the goal was to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level.
Abstract: This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The p...
TL;DR: The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors as mentioned in this paper, and most existing robust optimization approaches use outer-inner optimization.
Abstract: The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer–inner ...
TL;DR: A simulated annealing (SA) algorithm is proposed to solve the general share-a-ride problem (G-SARP), and the proposed SA algorithm outperforms basic SA and TS algorithms.
Abstract: This research introduces an extension of the share-a-ride problem (SARP), called the general share-a-ride problem (G-SARP). Similarly to SARP, a taxi in G-SARP can service passenger and package req...
TL;DR: A new methodology for the simultaneous optimization of placement and forces of friction dampers is proposed, and the results showed the excellent performance of the proposed method, reducing the root mean square displacement by more than 82%, with only three friction damper and in a relatively short computational time.
Abstract: It is well known that the use of passive energy dissipation devices such as friction dampers reduces the dynamic response of structures subjected to earthquakes. However, the parameters of each dam...
TL;DR: In this paper, a powertrain composed of four electric motors with different gear ratios to the differential of the rear axle is proposed to increase the overall output efficiency and reduce energy losses.
Abstract: The architecture and design of the propulsion system of electric vehicles are highly important for the reduction of energy losses. This work presents a powertrain composed of four electric motors in which each motor is connected with a different gear ratio to the differential of the rear axle. A strategy to reduce energy losses is proposed, in which two phases are applied. Phase 1 uses a divide-and-conquer approach to increase the overall output efficiency by obtaining the optimal torque distribution for the electric motors. Phase 2 applies a genetic algorithm to find the optimal value of the gear ratios, in which each individual of each generation applies Phase 1. The results show an optimized efficiency map for the output torque and speed of the powertrain. The increase in efficiency and the reduction of energy losses are validated by the use of numerical experiments in various driving cycles.
TL;DR: In this article, customer satisfaction and focusing on the most profitable customers are the key elements to achieving a sustainable competitive advantage in the current business environment, and the mixed-model assemb...
Abstract: Customer satisfaction and focusing on the most profitable customers are the key elements to achieving a sustainable competitive advantage in the current business environment. The mixed-model assemb...
TL;DR: Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.
Abstract: Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall–runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for...
TL;DR: The team orienteering problem with variable profits (TOPVP) is studied and an algorithm based on iterated local search (ILS) that is able to solve modified benchmark instances is proposed, concluding that ILS produces solutions which are comparable to those obtained by the commercial solver CPLEX for smaller instances.
Abstract: The orienteering problem (OP) is a routing problem that has numerous applications in various domains such as logistics and tourism. The objective is to determine a subset of vertices to visit for a vehicle so that the total collected score is maximized and a given time budget is not exceeded. The extensive application of the OP has led to many different variants, including the team orienteering problem (TOP) and the team orienteering problem with time windows. The TOP extends the OP by considering multiple vehicles. In this article, the team orienteering problem with variable profits (TOPVP) is studied. The main characteristic of the TOPVP is that the amount of score collected from a visited vertex depends on the duration of stay on that vertex. A mathematical programming model for the TOPVP is first presented and an algorithm based on iterated local search (ILS) that is able to solve modified benchmark instances is then proposed. It is concluded that ILS produces solutions which are comparable to those obtained by the commercial solver CPLEX for smaller instances. For the larger instances, ILS obtains good-quality solutions that have significantly better objective value than those found by CPLEX under reasonable computational times.
TL;DR: An improved multi-group swarm-based optimization method that can not only optimize the parameters of the lateral flight control system, but also find diversity solutions of the underlying optimization problem.
Abstract: Crosswind landing is one of the most complex and challenging landing manoeuvres; an aircraft can drift laterally or could even be blown off the runway under extreme crosswind conditions. This artic...
TL;DR: The method is compared with conventional swarm strategies able to solve problems with variable number of dimensions and novel performance metrics are defined in the article to evaluate convergence properties of the method.
Abstract: Some real-life optimization problems, apart from dependence on the combination of state variables, also show dependence on the complexity of the model describing the problem. Changing model complexity implies changing the number of decision space dimensions.A new method called Particle Swarm Optimization for Variable Number of Dimensions is developed here. The well-known particle swarm optimization procedure is modified to handle spaces with a variable number of dimensions within a single run. Some well-known benchmark problems are modified to depend on the number of dimensions. Novel performance metrics are defined in the article to evaluate convergence properties of the method. Some recommendations for setting the optimization are made according to results of the method on the proposed benchmark test suite. The method is compared with conventional swarm strategies able to solve problems with variable number of dimensions.
TL;DR: In this paper, the Galerkin projection technique in conjunction with the moment-matching equations is employed in DD-PCE for higher-dimensional uncertainty propagation, which is then compared with current PCE methods to fully investigate its relative merits through four numerical examples.
Abstract: As a novel type of polynomial chaos expansion (PCE), the data-driven PCE (DD-PCE) approach has been developed to have a wide range of potential applications for uncertainty propagation. While the research on DD-PCE is still ongoing, its merits compared with the existing PCE approaches have yet to be understood and explored, and its limitations also need to be addressed. In this article, the Galerkin projection technique in conjunction with the moment-matching equations is employed in DD-PCE for higher-dimensional uncertainty propagation. The enhanced DD-PCE method is then compared with current PCE methods to fully investigate its relative merits through four numerical examples considering different cases of information for random inputs. It is found that the proposed method could improve the accuracy, or in some cases leads to comparable results, demonstrating its effectiveness and advantages. Its application in dealing with a Mars entry trajectory optimization problem further verifies its effecti...
TL;DR: In this article, a hybrid method based on the combination of the GA and ANN is developed to optimize the design of three-dimensional (3-D) radiant furnaces, where the ANN is used to predict the objective function value which is trained through the data produced by applying the Monte Carlo method.
Abstract: In this article, a new hybrid method based on the combination of the genetic algorithm (GA) and artificial neural network (ANN) is developed to optimize the design of three-dimensional (3-D) radiant furnaces A 3-D irregular shape design body (DB) heated inside a 3-D radiant furnace is considered as a case study The uniform thermal conditions on the DB surfaces are obtained by minimizing an objective function An ANN is developed to predict the objective function value which is trained through the data produced by applying the Monte Carlo method The trained ANN is used in conjunction with the GA to find the optimal design variables The results show that the computational time using the GA-ANN approach is significantly less than that of the conventional method It is concluded that the integration of the ANN with GA is an efficient technique for optimization of the radiant furnaces