Shape optimal design of arch dams including dam-water–foundation rock interaction using a grading strategy and approximation concepts

Optimal design of arch dams including dam-water–foundation rock interaction is achieved using the soft computing techniques. For this, linear dynamic behavior of arch dam-water–foundation rock system subjected to earthquake ground motion is simulated using the finite element method at first and then, to reduce the computational cost of optimization process, a wavelet back propagation neural network (WBPNN) is designed to predict the arch dam response instead of directly evaluating it by a time-consuming finite-element analysis (FEA). In order to enhance the performance generality of the neural network, a dam grading technique (DGT) is also introduced. To assess the computational efficiency of the proposed methodology for arch dam optimization, an actual arch dam is considered. The optimization is implemented via the simultaneous perturbation stochastic approximation (SPSA) algorithm for the various conditions of the interaction problem. Numerical results show the merits of the suggested techniques for arch dam optimization. It is also found that considering the dam-water–foundation rock interaction has an important role for safely designing an arch dam.     

Advances in concrete arch dams shape optimization

This paper presents an efficient methodology to find the optimum shape of arch dams. In order to create the geometry of arch dams a new algorithm based on Hermit Splines is proposed. A finite element based shape sensitivity analysis for design-dependent loadings involving body force, hydrostatic pressure and earthquake loadings is implemented. The sensitivity analysis is performed using the concept of mesh design velocity. In order to consider the practical requirements in the optimization model such as construction stages, many geometrical and behavioral constrains are included in the model in comparison with previous researches. The optimization problem is solved via the sequential quadratic programming (SQP) method. The proposed methods are applied successfully to an Iranian arch dam, and good results are achieved. By using such methodology, efficient software for shape optimization of concrete arch dams for practical and reliable design now is available.     

A capable neural network model for solving the maximum flow problem

This paper presents an optimization technique for solving a maximum flow problem arising in widespread applications in a variety of settings. On the basis of the Karush–Kuhn–Tucker (KKT) optimality conditions, a neural network model is constructed. The equilibrium point of the proposed neural network is then proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the maximum flow problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

Stochastic dynamic lot-sizing problem using bi-level programming base on artificial intelligence techniques

Simulation is generally used to study non-deterministic problems in industry. When a simulation process finds the solution to an NP-hard problem, its efficiency is lowered, and computational costs increase. This paper proposes a stochastic dynamic lot-sizing problem with asymmetric deteriorating commodity, in which the optimal unit cost of material and unit holding cost would be determined. This problem covers a sub-problem of replenishment planning, which is NP-hard in the computational complexity theory. Therefore, this paper applies a decision system, based on an artificial neural network (ANN) and modified ant colony optimization (ACO) to solve this stochastic dynamic lot-sizing problem. In the methodology, ANN is used to learn the simulation results, followed by the application of a real-valued modified ACO algorithm to find the optimal decision variables. The test results show that the intelligent system is applicable to the proposed problem, and its performance is better than response surface methodology.     

A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling

Due to the implementation of government legislation, social responsibility, environmental concern, economic benefits and customer awareness the industries are under a great pressure not only to provide environmentally friendly products but also to take back the product after its use. The issue in reverse logistics is to take back the used products, either under warranty or at the end of use or at the end of lease, so that the products or its parts are appropriately disposed, recycled, reused or remanufactured. In order to overcome this issue, it is necessary to setup a logistics network for arising goods flow from end users to manufacturers. In this study, the optimum usage of secondary lead recovered from the spent lead–acid batteries for producing new battery is presented. The disposal in surface or sewage water or land of liquid content of the lead–acid batteries is strictly restricted. Because of the need for environmental protection and the lack of considerable lead resources, the spent batteries treatment and lead recovery are becoming crucial now-a-days. The objective of this paper is to develop a multi echelon, multi period, multi product closed loop supply chain network model for product returns and the decisions are made regarding material procurement, production, distribution, recycling and disposal. The proposed heuristics based genetic algorithm (GA) is applied as a solution methodology to solve mixed integer linear programming model (MILP). Finally the computational results obtained through GA are compared with the solutions obtained by GAMS optimization software. The solution reveals that the proposed methodology performs very well in terms of both quality of solutions obtained and computational time.     

Dynamic inversion method for the material parameters of a high arch dam and its foundation

The overall mechanical behavior of the structure of an arch dam is comprehensively reflected by the vibration modal information included in measured vibration response. Hence, the results obtained from inverting material parameters based on measured vibration data are often superior to those based on static monitoring data. In this study, a dynamic inversion method for the material parameters of a high arch dam and its foundation is proposed on the basis of the measured vibration response. First, an arch dam prototype test is conducted to obtain the measured dynamic displacement response as input. Then, a stochastic subspace identification method based on singular entropy is formulated to determine the modal parameters. Second, a dynamic elastic modulus (DEM) with a great influence on the modal parameters is selected as the material parameter to be inverted. Then, a response surface model (RSM), which reflects the nonlinear relationship between the material and modal parameters of each zone, is constructed. Latin hypercube sampling is used to generate the sample library of the DEM. The RSM is fitted by modal parameters calculated on the basis of the arch dam finite element model (FEM) and is applied to replace the FEM. Finally, the optimization mathematical model of the inversion of the DEM is established. Then, the objective function is optimized through a genetic algorithm, and the optimal combination of the DEM in each zone is inverted. The modal parameters of the arch dam calculated by inversion results are consistent with those measured by variation law and values. Therefore, the inversion results are reasonable and reliable. This method provides a new idea for determining the material parameters of a high arch dam and its foundation during the operation period.     

Nonlinear finite element reliability analysis of Concrete-Faced Rockfill (CFR) dams under static effects

The response of concrete slab on Concrete-Faced Rockfill (CFR) dams is very important. This study investigates the reliability of the concrete slab on a CFR dam by the improved Rackwitz–Fiessler method under static loads. For this purpose, ANSYS finite element analysis software and FERUM reliability analysis program are combined with direct coupled method and response surface method. Reliability index and probability of failure of the concrete are computed in the all critical points of the concrete slab by dam height. This study is also expanded for the reliability of CFR dams including different concrete slab thickness. In addition to the linear behavior, geometrically and materially non-linear responses of the dam are considered in the finite element analysis which is performed with reliability analysis. The Drucker–Prager method and the multi linear kinematic hardening method are, respectively, used for concrete slab and for rockfill and foundation rock. Finite element model used in the analyses includes dam–reservoir–foundation interaction. Reservoir water is modeled by the Lagrangian approach. Welded and friction contact based on the Coulomb’s friction law are considered in the joints of the dam. One-dimensional two noded contact elements are used to define friction. The self-weight of the dam and the hydrostatic pressure of the reservoir water are considered in the numerical solutions. According to this study, hydrostatic pressure, nonlinear response of the rockfill and the decrease in the concrete slab thickness reduce the reliability of the concrete slab of the CFR dam. Besides, the CFR dam models including friction are safer than the models including welded contact in the joints.     

Multi-objective analysis of dam release flows in rivers downstream from hydropower reservoirs

This paper assesses dam releases from hydropower reservoirs in order to optimize power production and fish habitat protection. A multi-objective programming model includes output from 2-D hydraulic simulation for habitat assessment to optimize power production and fish habitat suitability as a Pareto set. To identify the optimal Pareto set two different approaches are used and compared: ε-constraint methods and non-dominant-sorting genetic algorithm (NSGA II). To formulate the ecological objective the river habitat quality is quantified by the weighted usable area (WUA). The relation between the WUA and the river flow-rate is obtained by using a 2D hydraulic model in which the hydraulic characteristics of river current – depth and velocity – are calculated by a finite difference numerical integration of two-dimensional shallow water equations on a boundary fitted non orthogonal curvilinear mesh. This approach allows the integration of motion equations on geometrically complex domains as those representing the morphology of natural watercourses. The performance of the proposed methodology is analyzed in a case study of a stretch of the Piave river downstream of the dam of the Pieve di Cadore reservoir (Belluno, Italy).     

Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness

The prediction of surface roughness is a challengeable problem. In order to improve the prediction accuracy in end milling process, an improved approach is proposed to model surface roughness with adaptive network-based fuzzy inference system (ANFIS) and leave-one-out cross-validation (LOO-CV) approach. This approach focuses on both architecture and parameter optimization. LOO-CV, which is an effective measure to evaluate the generalization capability of mode, is employed to find the most suitable membership function and the optimal rule base of ANFIS model for the issue of surface roughness prediction. To find the optimal rule base of ANFIS, a new “top down” rules reduction method is suggested. Three machining parameters, the spindle speed, feed rate and depth of cut are used as inputs in the model. Based on the same experimental data, the predictive results of ANFIS with LOO-CV are compared with the results reported recently in the literature and ANFIS with clustering methods. The comparisons indicate that the presented approach outperforms the opponent methods, and the prediction accuracy can be improved to 96.38%. ANFIS with LOO-CV approach is an effective approach for prediction of surface roughness in end milling process.     

Structural optimization by wavelet transforms and neural networks

An efficient methodology is presented to achieve optimal design of structures for earthquake loading. In this methodology a combination of wavelet transforms, neural networks and evolutionary algorithms are employed. The stochastic nature of the evolutionary algorithms makes the slow convergence. Specially, when earthquake induced loads are taken into account. To reduce the computational burden, a discrete wavelet transform is used by means of which the number of points in the earthquake record is decreased. Then, by using a surrogate model, the dynamic responses of the structures are predicted. In order to investigate the efficiency of the proposed methodology, two structures are designed for optimal weight. The numerical results demonstrate the computational advantages of the proposed hybrid methodology to optimal dynamic design of structures.     

Box-constrained multi-objective optimization: A gradient-like method without “a priori” scalarization

The aim of this paper is the development of an algorithm to find the critical points of a box-constrained multi-objective optimization problem. The proposed algorithm is an interior point method based on suitable directions that play the role of gradient-like directions for the vector objective function. The method does not rely on an “a priori” scalarization and is based on a dynamic system defined by a vector field of descent directions in the considered box. The key tool to define the mentioned vector field is the notion of vector pseudogradient. We prove that the limit points of the solutions of the system satisfy the Karush–Kuhn–Tucker (KKT) first order necessary condition for the box-constrained multi-objective optimization problem. These results allow us to develop an algorithm to solve box-constrained multi-objective optimization problems. Finally, we consider some test problems where we apply the proposed computational method. The numerical experience shows that the algorithm generates an approximation of the local optimal Pareto front representative of all parts of optimal front.     

A simplified recurrent neural network for pseudoconvex optimization subject to linear equality constraints

In this paper, the optimization techniques for solving pseudoconvex optimization problems are investigated. A simplified recurrent neural network is proposed according to the optimization problem. We prove that the optimal solution of the optimization problem is just the equilibrium point of the neural network, and vice versa if the equilibrium point satisfies the linear constraints. The proposed neural network is proven to be globally stable in the sense of Lyapunov and convergent to an exact optimal solution of the optimization problem. A numerical simulation is given to illustrate the global convergence of the neural network. Applications in business and chemistry are given to demonstrate the effectiveness of the neural network.     

Discrete Capacity Assignment in IP networks using Particle Swarm Optimization

This paper presents a design methodology for IP networks under end-to-end Quality-of-Service (QoS) constraints. Particularly, we consider a more realistic problem formulation in which the link capacities of a general-topology packet network are discrete variables. This Discrete Capacity Assignment (DCA) problem can be classified as a constrained combinatorial optimization problem. A refined TCP/IP traffic modeling technique is also considered in order to estimate performance metrics for networks loaded by realistic traffic patterns. We propose a discrete variable Particle Swarm Optimization (PSO) procedure to find solutions for the problem. A simple approach called Bottleneck Link Heuristic (BLH) is also proposed to obtain admissible solutions in a fast way. The PSO performance, compared to that one of an exhaustive search (ES) procedure, suggests that the PSO algorithm provides a quite efficient approach to obtain (near) optimal solutions with small computational effort.     

An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada,United Kingdom,and South Korea

This paper presents an adaptive network based fuzzy inference system (ANFIS)–auto regression (AR)–analysis of variance (ANOVA) algorithm to improve oil consumption estimation and policy making. ANFIS algorithm is developed by different data preprocessing methods and the efficiency of ANFIS is examined against auto regression (AR) in Canada, United Kingdom and South Korea. For this purpose, mean absolute percentage error (MAPE) is used to show the efficiency of ANFIS. The algorithm for calculating ANFIS performance is based on its closed and open simulation abilities. Moreover, it is concluded that ANFIS provides better results than AR in Canada, United Kingdom and South Korea. This is unlike previous expectations that auto regression always provides better estimation for oil consumption estimation. In addition, ANOVA is used to identify policy making strategies with respect to oil consumption. This is the first study that introduces an integrated ANFIS–AR–ANOVA algorithm with preprocessing and post processing modules for improvement of oil consumption estimation in industrialized countries.     

Embedding evolutionary strategy in ordinal optimization for hard optimization problems

This work proposes a method for embedding evolutionary strategy (ES) in ordinal optimization (OO), abbreviated as ESOO, for solving real-time hard optimization problems with time-consuming evaluation of the objective function and a huge discrete solution space. Firstly, an approximate model that is based on a radial basis function (RBF) network is utilized to evaluate approximately the objective value of a solution. Secondly, ES associated with the approximate model is applied to generate a representative subset from a huge discrete solution space. Finally, the optimal computing budget allocation (OCBA) technique is adopted to select the best solution in the representative subset as the obtained “good enough” solution. The proposed method is applied to a hotel booking limits (HBL) problem, which is formulated as a stochastic combinatorial optimization problem with a huge discrete solution space. The good enough booking limits, obtained by the proposed method, have promising solution quality, and the computational efficiency of the method makes it suitable for real-time applications. To demonstrate the computational efficiency of the proposed method and the quality of the obtained solution, it is compared with two competing methods – the canonical ES and the genetic algorithm (GA). Test results demonstrate that the proposed approach greatly outperforms the canonical ES and GA.     

Hybrid approach for Pareto front expansion in heuristics

Heuristic search can be an effective multi-objective optimization tool; however, the required frequent function evaluations can exhaust computational sources. This paper explores using a hybrid approach with statistical interpolation methods to expand optimal solutions obtained by multiple criteria heuristic search. The goal is to significantly increase the number of Pareto optimal solutions while limiting computational effort. The interpolation approaches studied are kriging and general regression neural networks. This paper develops a hybrid methodology combining an interpolator with a heuristic, and examines performance on several non-linear bi-objective example problems. Computational experience shows this approach successfully expands and enriches the Pareto fronts of multi-objective optimization problems.     

A self-organising model for the travelling salesman problem

This work describes a new algorithm, based on a self-organising neural network approach, to solve the Travelling Salesman Problem (TSP). Firstly, various features of the available adaptive neural network algorithms for TSP are reviewed and a new algorithm is proposed. In order to investigate the performance of the algorithms, a comprehensive empirical study has been provided. The simulations, which are conducted on a series of standard data, evaluate the overall performance of this approach by comparing the results with the best known or the optimal solutions of the problems. The proposed algorithm shows significant advances in both the quality of the solution and computational effort for most of the experimental data. The deviation from the optimal solution of this algorithm was, in the worst case, around 2%. This fact indicates that the self-organising neural network may be regarded as a promising heuristic approach for optimisation problems.     

Elasto-plastic earthquake response of arch dams including fluid–structure interaction by the Lagrangian approach

Elasto-plastic earthquake response of arch dams including fluid–structure interaction by the Lagrangian approach is mainly investigated in this study. To this aim, three-dimensional eight-noded version of Lagrangian fluid finite element including the effects of compressible wave propagation and surface sloshing motion, and three-dimensional version of Drucker–Prager model based on associated flow rule assumption were programmed in FORTRAN language by authors and incorporated into the program NONSAP. Two new components added into the NONSAP were tested on a simple fluid tank and a simple fluid–structure system and obtained very reasonable results.     

Response surface methodology: A neural network approach

Response surface methodology is used to optimize the parameters of a process when the function that describes it is unknown. The procedure involves fitting a function to the given data and then using optimization techniques to obtain the optimal parameters. This procedure is usually difficult due to the fact that obtaining the right model may not be possible or at best very time consuming.In this paper, a two-stage procedure for obtaining the best parameters for a process with an unknown model is developed. The procedure is based on implementing response surface methodology via neural networks. Two neural networks are trained: one for the unknown function and the other for derivatives of this function which are computed using the first neural network. These neural networks are then used iteratively to compute parameters for an equation which is ultimately used for optimizing the function. Results of some simulation studies are also presented.     

Controlling the chaotic response to a prospective external signal using back-propagation neural networks

For a chaotic system, a control scheme is presented, based on the back-propagation neural network (BPNN). The scheme can control the chaotic response to a prospective external signal, which can be periodic, nonlinear or even a non-analytical discontinuous function. For a chaotic system with high dimensions, each variable can be controlled for the different signals. For Lorenz, Rossler and Duffing systems, simulations are carried out and the proposed scheme is proved to be effective within a short control time.