Showing papers in "Engineering Optimization in 2016"
TL;DR: In this paper, a double global optimum genetic algorithm and particle swarm optimization (GA-PSO) based approach is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path.
Abstract: Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm–particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
113 citations
TL;DR: In this article, an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices is presented.
Abstract: This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices. The proposed approach employs PSO to minimize the total energy and operating cost of the microgrid via optimal adjustment of the control variables of the EOM, while satisfying various operating constraints. Owing to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties and market prices, a probabilistic approach in the EOM is introduced. The proposed method is examined and tested on a typical grid-connected microgrid including fuel cell, gas-fired microturbine, wind turbine, photovoltaic and energy storage devices. The obtained results prove the efficiency of the proposed approach to solve the EOM of the microgrids.
88 citations
TL;DR: In this article, a robotic U-shaped assembly line balancing (RUALB) algorithm is proposed to assign robots to workstations to perform multiple tasks on a U-shape assembly line.
Abstract: Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure ...
81 citations
TL;DR: In this article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs.
Abstract: In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error = 3.362 and root mean square error = 0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network...
73 citations
TL;DR: In this work, a method to manage variable speed limits combined with coordinated ramp metering within the framework of the Lighthill–Whitham–Richards (LWR) network model is introduced and the switch of speeds at certain fixed points in time is explained.
Abstract: The control of traffic flow can be related to different applications. In this work, a method to manage variable speed limits combined with coordinated ramp metering within the framework of the Lighthill–Whitham–Richards (LWR) network model is introduced. Following a ‘first-discretize-then-optimize’ approach, the first order optimality system is derived and the switch of speeds at certain fixed points in time is explained, together with the boundary control for the ramp metering. Sequential quadratic programming methods are used to solve the control problem numerically. For application purposes, experimental setups are presented wherein variable speed limits are used as a traffic guidance system to avoid traffic jams on highway interchanges and on-ramps.
66 citations
TL;DR: Comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, and the results showed that the VOA is a viable solution for continuous optimization.
Abstract: A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called ‘antivirus’) to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, parti...
57 citations
TL;DR: In this paper, an improved version of the teaching-learning-based optimization (TLBO) algorithm is proposed for truss topology optimization (TTO), with static and dynamic constraints on planar and space trusses.
Abstract: In this study, an improved version of the teaching–learning-based optimization (TLBO) algorithm is proposed for truss topology optimization (TTO), with static and dynamic constraints on planar and space trusses. The basic TLBO algorithm is improved to enhance its exploration and exploitation abilities by considering various factors such as the number of teachers, adaptive teaching, tutorial learning and self-motivated learning. The TTO problems are considered with multiple load conditions and subjected to constraints for natural frequencies, element stresses, nodal displacements, Euler buckling criteria and kinematic stability conditions. TTO is achieved with the removal of superfluous elements and nodes from the ground structure, and results in a mass saving. In this method, difficulties arise owing to singular solution and unnecessary analysis; therefore, the finite element model is reformed to resolve these issues. A single-stage optimization approach is used, in which size and topology optimization ar...
56 citations
TL;DR: In this paper, the authors proposed a methodology to simultaneously optimize the location of friction dampers and their friction forces in structures subjected to seismic loading, to achieve a desired level of reduction in the response.
Abstract: It is known that the use of passive energy-dissipation devices, such as friction dampers, reduces considerably the dynamic response of a structure subjected to earthquake ground motions. Nevertheless, the parameters of each damper and the best placement of these devices remain difficult to determine. Some articles on optimum design of tuned mass dampers and viscous dampers have been published; however, there is a lack of studies on optimization of friction dampers. The main contribution of this article is to propose a methodology to simultaneously optimize the location of friction dampers and their friction forces in structures subjected to seismic loading, to achieve a desired level of reduction in the response. For this purpose, the recently developed backtracking search optimization algorithm (BSA) is employed, which can deal with optimization problems involving mixed discrete and continuous variables. For illustration purposes, two different structures are presented. The first is a six-storey shear bu...
51 citations
TL;DR: A new Kriging-based optimization method that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region and this trust region is adjusted depending on the ratio of the actual improvement to the EI.
Abstract: The Kriging-based Efficient Global Optimization (EGO) method works well on many expensive black-box optimization problems. However, it does not seem to perform well on problems with steep and narrow global minimum basins and on high-dimensional problems. This article develops a new Kriging-based optimization method called TRIKE (Trust Region Implementation in Kriging-based optimization with Expected improvement) that implements a trust-region-like approach where each iterate is obtained by maximizing an Expected Improvement (EI) function within some trust region. This trust region is adjusted depending on the ratio of the actual improvement to the EI. This article also develops the Kriging-based CYCLONE (CYClic Local search in OptimizatioN using Expected improvement) method that uses a cyclic pattern to determine the search regions where the EI is maximized. TRIKE and CYCLONE are compared with EGO on 28 test problems with up to 32 dimensions and on a 36-dimensional groundwater bioremediation application i...
49 citations
TL;DR: This study attempts to enhance the performance of the FFA by suggesting two new expressions for the attractiveness and randomness parameters of the algorithm, and its performance is compared with standard FFA as well as with particle swarm and cuckoo search algorithms.
Abstract: Mathematical modelling of real-world-sized steel frames under the Load and Resistance Factor Design–American Institute of Steel Construction (LRFD-AISC) steel design code provisions, where the steel profiles for the members are selected from a table of steel sections, turns out to be a discrete nonlinear programming problem. Finding the optimum design of such design optimization problems using classical optimization techniques is difficult. Metaheuristic algorithms provide an alternative way of solving such problems. The firefly algorithm (FFA) belongs to the swarm intelligence group of metaheuristics. The standard FFA has the drawback of being caught up in local optima in large-sized steel frame design problems. This study attempts to enhance the performance of the FFA by suggesting two new expressions for the attractiveness and randomness parameters of the algorithm. Two real-world-sized design examples are designed by the enhanced FFA and its performance is compared with standard FFA as well as with pa...
46 citations
TL;DR: In this article, the authors extend the sequential optimization and reliability analysis (SORA) method to time-dependent problems with both stationary stochastic process loads and random variables.
Abstract: Time-dependent reliability-based design ensures the satisfaction of reliability requirements for a given period of time, but with a high computational cost. This work improves the computational efficiency by extending the sequential optimization and reliability analysis (SORA) method to time-dependent problems with both stationary stochastic process loads and random variables. The challenge of the extension is the identification of the most probable point (MPP) associated with time-dependent reliability targets. Since a direct relationship between the MPP and reliability target does not exist, this work defines the concept of equivalent MPP, which is identified by the extreme value analysis and the inverse saddlepoint approximation. With the equivalent MPP, the time-dependent reliability-based design optimization is decomposed into two decoupled loops: deterministic design optimization and reliability analysis, and both are performed sequentially. Two numerical examples are used to show the efficiency of ...
TL;DR: In this paper, the authors present a comprehensive mathematical formulation model for a short-term open-pit mine block sequencing problem, which considers nearly all relevant technical aspects in open pit mining.
Abstract: This study presents a comprehensive mathematical formulation model for a short-term open-pit mine block sequencing problem, which considers nearly all relevant technical aspects in open-pit mining. The proposed model aims to obtain the optimum extraction sequences of the original-size (smallest) blocks over short time intervals and in the presence of real-life constraints, including precedence relationship, machine capacity, grade requirements, processing demands and stockpile management. A hybrid branch-and-bound and simulated annealing algorithm is developed to solve the problem. Computational experiments show that the proposed methodology is a promising way to provide quantitative recommendations for mine planning and scheduling engineers.
TL;DR: A new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the ABC algorithm.
Abstract: The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.
TL;DR: In this paper, a numerical method is developed to obtain the optimum design of a cable-stayed bridge with minimum cost, minimum deflections and minimum stresses. But the method is not suitable for the case of large bridges and it cannot account for all the relevant effects (concrete timedependent effects, construction stages and geometrical nonlinear effects).
Abstract: The design of cable-stayed bridges involves a significant number of design variables and design objectives. The concrete cable-stayed bridge optimization is formulated here as a multi-objective optimization problem with objectives of minimum cost, minimum deflections and minimum stresses. A numerical method is developed to obtain the optimum design of such structures. This numerical method includes: structural analysis, sensitivity analysis and optimization. The structural analysis accounts for all the relevant effects (concrete time-dependent effects, construction stages and geometrical nonlinear effects). The structural response to changes in the design variables is achieved by a discrete direct sensitivity analysis procedure, and an entropy-based approach was used for structural optimization. The features and applicability of the proposed method are demonstrated by numerical examples concerning the optimization of a real-sized concrete cable-stayed bridge.
TL;DR: In this article, the authors presented an efficient approach for reliability-based topology optimization (RBTO) in which the computational effort involved in solving the RBTO problem is equivalent to that of solving a deterministic topology optimisation (DTO) problem.
Abstract: This article presents an efficient approach for reliability-based topology optimization (RBTO) in which the computational effort involved in solving the RBTO problem is equivalent to that of solving a deterministic topology optimization (DTO) problem. The methodology presented is built upon the bidirectional evolutionary structural optimization (BESO) method used for solving the deterministic optimization problem. The proposed method is suitable for linear elastic problems with independent and normally distributed loads, subjected to deflection and reliability constraints. The linear relationship between the deflection and stiffness matrices along with the principle of superposition are exploited to handle reliability constraints to develop an efficient algorithm for solving RBTO problems. Four example problems with various random variables and single or multiple applied loads are presented to demonstrate the applicability of the proposed approach in solving RBTO problems. The major contribution of this a...
TL;DR: It is demonstrated that, with respect to model formulation, the number of linear and nonlinear equations involved in water distribution networks can be reduced to theNumber of closed simple loops.
Abstract: In this study it is demonstrated that, with respect to model formulation, the number of linear and nonlinear equations involved in water distribution networks can be reduced to the number of closed simple loops. Regarding the optimization technique, a discrete state transition algorithm (STA) is introduced to solve several cases of water distribution networks. Firstly, the focus is on a parametric study of the ‘restoration probability and risk probability’ in the dynamic STA. To deal effectively with head pressure constraints, the influence is then investigated of the penalty coefficient and search enforcement on the performance of the algorithm. Based on the experience gained from training the Two-Loop network problem, a discrete STA has successfully achieved the best known solutions for the Hanoi, triple Hanoi and New York network problems.
TL;DR: A novel version of the discrete harmonySearch (DHS) algorithm, namely fuzzy discrete harmony search (FDHS), is proposed for optimizing capacitor placement in distribution systems, which is the first application of DHS to specify appropriate capacitor locations and their best amounts in the distribution systems.
Abstract: Similarly to other optimization algorithms, harmony search (HS) is quite sensitive to the tuning parameters. Several variants of the HS algorithm have been developed to decrease the parameter-dependency character of HS. This article proposes a novel version of the discrete harmony search (DHS) algorithm, namely fuzzy discrete harmony search (FDHS), for optimizing capacitor placement in distribution systems. In the FDHS, a fuzzy system is employed to dynamically adjust two parameter values, i.e. harmony memory considering rate and pitch adjusting rate, with respect to normalized mean fitness of the harmony memory. The key aspect of FDHS is that it needs substantially fewer iterations to reach convergence in comparison with classical discrete harmony search (CDHS). To the authors’ knowledge, this is the first application of DHS to specify appropriate capacitor locations and their best amounts in the distribution systems. Simulations are provided for 10-, 34-, 85- and 141-bus distribution systems using CDHS ...
TL;DR: In this paper, the authors proposed the modified differential evolution algorithm (DE-C) and differential evolution (DE) algorithm to solve a simple assembly line balancing problem type 1 and SALBP-1 when the maximum number of machine types in a workstation is considered.
Abstract: This article proposes the differential evolution algorithm (DE) and the modified differential evolution algorithm (DE-C) to solve a simple assembly line balancing problem type 1 (SALBP-1) and SALBP-1 when the maximum number of machine types in a workstation is considered (SALBP-1M). The proposed algorithms are tested and compared with existing effective heuristics using various sets of test instances found in the literature. The computational results show that the proposed heuristics is one of the best methods, compared with the other approaches.
TL;DR: In this paper, the authors describe a method using the supply chain construct for designing power grids that are relatively insensitive to failure in the integrated generation and transmission system, and the efficacy of the method is illustra...
Abstract: A power grid is vulnerable and failures are inevitable. Failures decrease the power supply with an adverse effect on meeting the demand for electricity. Therefore, there is a need for a method to design power grid networks that result in the least possible disruption to the power supply when a failure occurs. In the literature, the focus has been on the design of the generation system without considering the transmission system or failures in the transmission system. Since power grids are integrated generation and transmission systems, each system will affect the other, so both generation and transmission systems need to be considered, as they are in this article. Methods developed for the structural modelling and analysis of supply chains are shown to be useful. The focus in this article is on describing a method using the supply chain construct for designing power grids that are relatively insensitive to failure in the integrated generation and transmission system. The efficacy of the method is illustra...
TL;DR: In this paper, the bi-directional evolutionary structural optimization (BESO) method based on the element-free Galerkin (EFG) method is presented for topology optimization of continuum structures.
Abstract: In this article, the bi-directional evolutionary structural optimization (BESO) method based on the element-free Galerkin (EFG) method is presented for topology optimization of continuum structures. The mathematical formulation of the topology optimization is developed considering the nodal strain energy as the design variable and the minimization of compliance as the objective function. The EFG method is used to derive the shape functions using the moving least squares approximation. The essential boundary conditions are enforced by the method of Lagrange multipliers. Several topology optimization problems are presented to show the effectiveness of the proposed method. Many issues related to topology optimization of continuum structures, such as chequerboard patterns and mesh dependency, are studied in the examples.
TL;DR: In this paper, a fast evolutionary optimization strategy, named the cuckoo optimization algorithm, is proposed to solve the structural damage identification problem, which is based on computing static displacements by the flexibility matrix.
Abstract: This article presents an effective method for structural damage identification. The damage diagnosis problem is introduced as an optimization problem which is based on computing static displacements by the flexibility matrix. By utilizing this matrix, the complexity of the static displacement measurements in real cases can be overcome. The optimization problem is solved by a fast evolutionary optimization strategy, named the cuckoo optimization algorithm. The performance of the presented method was demonstrated by studying the benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring, and a numerical example of a frame. Moreover, the robustness of the presented approach was investigated in the presence of some prevalent modelling errors, and also when noisy and incomplete modal data are available. Finally, the efficiency of the proposed method was verified by an experimental study of a five-storey shear building structure. All the obtained results show the good performance of ...
TL;DR: The obtained results show that the MOICA outperforms most of the methods available in the literature, and can also handle multi-objective engineering design problems with high dimensions.
Abstract: This article proposes an improved imperialistic competitive algorithm to solve multi-objective optimization problems. The proposed multi-objective imperialistic competitive algorithm (MOICA) uses the elitist strategy, based on the mutation and crossover as in genetic algorithms, and the Pareto concept to store simultaneously optimal solutions of multiple conflicting functions. Three performance metrics are used to evaluate the performance of the new algorithm: convergence to the true Pareto-optimal set, solution diversity and robustness, characterized by the variance over 10 runs. To validate the efficiency of the proposed algorithm, several multi-objective standard test functions with true solutions are used. The obtained results show that the MOICA outperforms most of the methods available in the literature. The proposed algorithm can also handle multi-objective engineering design problems with high dimensions.
TL;DR: In this paper, a model for valve setting in water distribution networks (WDNs) with the aim of reducing the level of leakage is presented, based on the harmony search (HS) optimization algorithm.
Abstract: This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.
TL;DR: A new heuristic based on the Nawaz–Enscore–Ham algorithm is proposed in this article for solving a permutation flow-shop scheduling problem and better solution quality is illustrated compared to existing benchmark heuristics.
Abstract: A new heuristic based on the Nawaz–Enscore–Ham algorithm is proposed in this article for solving a permutation flow-shop scheduling problem. A new priority rule is proposed by accounting for the average, mean absolute deviation, skewness and kurtosis, in order to fully describe the distribution style of processing times. A new tie-breaking rule is also introduced for achieving effective job insertion with the objective of minimizing both makespan and machine idle time. Statistical tests illustrate better solution quality of the proposed algorithm compared to existing benchmark heuristics.
TL;DR: Perimeter-based Coverage Optimization protocol (PeCO) is proposed, a hybrid of centralized and distributed methods: the region of interest is first subdivided into subregions and the protocol is then distributed among sensor nodes in each subregion.
Abstract: The most important problem in a Wireless Sensor Network (WSN) is to optimize the use of its limited energy provision, so that it can fulfil its monitoring task as long as possible. Among known available approaches that can be used to improve power management, lifetime coverage optimization provides activity scheduling which ensures sensing coverage while minimizing the energy cost. In this article an approach called Perimeter-based Coverage Optimization protocol (PeCO) is proposed. It is a hybrid of centralized and distributed methods: the region of interest is first subdivided into subregions and the protocol is then distributed among sensor nodes in each subregion. The novelty of the approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter-coverage level to schedule sensors' activities. Extensive simulation experiments demonstrate that PeCO can offer longer lifetime coverage for WSNs compared to other protocols.
TL;DR: In this article, the authors developed a hybrid reliability analysis method so that the probability analysis (PA) loop and interval analysis (IA) loop are decomposed into two separate loops, and the gradient projection method is applied to solve the extreme responses of the limit state function with respect to the interval variables.
Abstract: Random and interval variables often coexist. Interval variables make reliability analysis much more computationally intensive. This work develops a new hybrid reliability analysis method so that the probability analysis (PA) loop and interval analysis (IA) loop are decomposed into two separate loops. An efficient PA algorithm is employed, and a new efficient IA method is developed. The new IA method consists of two stages. The first stage is for monotonic limit-state functions. If the limit-state function is not monotonic, the second stage is triggered. In the second stage, the limit-state function is sequentially approximated with a second order form, and the gradient projection method is applied to solve the extreme responses of the limit-state function with respect to the interval variables. The efficiency and accuracy of the proposed method are demonstrated by three examples.
TL;DR: This article reviews a particular technique of this type, namely, shape-preserving response prediction (SPRP), which works on the level of the model responses to correct the underlying low-fidelity models.
Abstract: Computer simulation models are ubiquitous in modern engineering design. In many cases, they are the only way to evaluate a given design with sufficient fidelity. Unfortunately, an added computational expense is associated with higher fidelity models. Moreover, the systems being considered are often highly nonlinear and may feature a large number of designable parameters. Therefore, it may be impractical to solve the design problem with conventional optimization algorithms. A promising approach to alleviate these difficulties is surrogate-based optimization (SBO). Among proven SBO techniques, the methods utilizing surrogates constructed from corrected physics-based low-fidelity models are, in many cases, the most efficient. This article reviews a particular technique of this type, namely, shape-preserving response prediction (SPRP), which works on the level of the model responses to correct the underlying low-fidelity models. The formulation and limitations of SPRP are discussed. Applications to several en...
TL;DR: In this article, a simple reliability-based topology optimization (RBTO) methodology for continuum structures is investigated, where the two-layer nesting involved in RBTO is decoupled by the use of a particular optimization procedure.
Abstract: The structural configuration obtained by deterministic topology optimization may represent a low reliability level and lead to a high failure rate. Therefore, it is necessary to take reliability into account for topology optimization. By integrating reliability analysis into topology optimization problems, a simple reliability-based topology optimization (RBTO) methodology for continuum structures is investigated in this article. The two-layer nesting involved in RBTO, which is time consuming, is decoupled by the use of a particular optimization procedure. A topology description function approach (TOTDF) and a first order reliability method are employed for topology optimization and reliability calculation, respectively. The problem of the non-smoothness inherent in TOTDF is dealt with using two different smoothed Heaviside functions and the corresponding topologies are compared. Numerical examples demonstrate the validity and efficiency of the proposed improved method. In-depth discussions are also prese...
TL;DR: A novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP, which can significantly improve the modelling efficiency and provide satisfactory performance in terms of approximation accuracy.
Abstract: Radial basis function (RBF) surrogate models have been widely applied in engineering design optimization problems to approximate computationally expensive simulations. Ensemble of radial basis functions (ERBF) using the weighted sum of stand-alone RBFs improves the approximation performance. To achieve a good trade-off between the accuracy and efficiency of the modelling process, this article presents a novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP. Several numerical benchmark functions are utilized to test the performance of the proposed ERBF-QP method. The results show that ERBF-QP can significantly improve the modelling efficiency compared with several existing ERBF methods. Moreover, ERBF-QP also provides satisfactory performance in terms of approximation accuracy. Finally, the ERBF-QP method is applied to a satellite multidisciplinary design optimization problem to illustrate its practicality and effectiveness for real-world e...
TL;DR: In this article, the shape optimization of a low specific speed centrifugal pump at the design point is described, where a back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array.
Abstract: This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bezier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier–Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.