Global convergence of QPFTH method for large-scale nonlinear sparse constrained optimization

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

We present a class of hybrid methods for large-scale nonlinear optimization which allows for a smooth transition between the limited memory BFGS and truncated Newton methods. Numerical experiments suggest that the resulting methods in the class are more robust than the parent methods, in the sense that the methods solve more of the problems tested, at a moderate computational cost.KeywordsTruncated Newton MethodLimited Memory BFGSModerate Computational CostHybrid Method ClassHessian-vector ProductsThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this note, the hybrid method (combination of the homotopy perturbation method (HPM) and the Gauss elimination method (GEM)) is developed as a semi-analytical solution for the first kind system of Cauchy-type singular integral equations (CSIEs) with constant coefficients. Before applying the HPM, we have to first reduce the system of CSIEs into a triangle system of algebraic equations using GEM, which is then carried out using the HPM. Using the theory of the bounded, unbounded and semi-bounded solutions of CSIEs, we are able to find inverse operators for the system of CSIEs of the first kind. A stability analysis and convergent of the proposed method has been conducted in the weighted Lp space. Moreover, the proposed method is proven to be exact in the Holder class of functions for the system of characteristic SIEs for any type of initial guess. For each of the four cases, several examples are provided and examined to demonstrate the proposed method’s validity and accuracy. Obtained results are compared with the Chebyshev collocation method and modified HPM (MHPM). Example 3 reveals that the error term of the MHPM is slightly superior to that of the HPM. One of the features of the proposed method is that it can be solved as a complex-valued system of CSIEs. Numerical results revealed that the hybrid method dominates others.

An N-S/DSMC hybrid algorithm with chemical non-equilibrium is present by developing the MPC technique using current CFD and DSMC method based on same chemical reaction model. DSMC region is selected automatically according to CFD results in this hybrid scheme based on local Knudson numbers. A sub-relax technique is developed to deal with information exchange between CFD and DSMC to restrain the effect of statistical fluctuation from DSMC to CFD. This extends the application range of the DSMC and CFD method, and provides a way to predict the chemical non-equilibrium characteristics in near-continuum transitional flow regime. Hypersonic flow with chemical non-equilibrium around a 2D cylinder is simulated by the present N-S/DSMC hybrid method. Comparison shows that hybrid results are good agreement with full DSMC results in flow structure, non-equilibrium phenomenon, surface parameters and aerodynamics characteristics. This verifies the efficient adaptability and reliability of the present N-S/DSMC hybrid algorithm in simulating hypersonic flows for the transitional flow regime.

PurposeConstruction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.Design/methodology/approachDue to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.FindingsAccording to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.Practical implicationsICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.Originality/valueIn this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi-shell schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called Spherical Code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt greedy method, a Mixed Integer Linear Programming (MILP) method, and a Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state-of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool.

Flow through open channels can contain solids. The deposition of solids occasionally occurs due to insufficient flow velocity to transfer the solid particles, causing many problems with transfer systems. Therefore, a method to determine the limiting velocity (i.e. Fr) is required. In this paper, three alternative, hybrid evolutionary algorithm methods, including differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) based on the adaptive network-based fuzzy inference system are presented: ANFIS-GA, ANFIS-DE and ANFIS-PSO. In these methods, evolutionary algorithms optimize the membership functions, and ANFIS adjusts the premises and consequent parameters to optimize prediction performance. The performance of the proposed methods is compared with that of the general ANFIS using three different datasets comprising a wide range of data. The results show that the hybrid models (ANFIS-GA, ANFIS-DE and ANFIS-PSO) are more accurate than general ANFIS in training with a hybrid algorithm (hybrid of back propagation and least squares). Among the evolutionary algorithms, ANFIS-PSO performed the best (R2=0.976, RMSE=0.26, MARE=0.057, BIAS=-0.004 and SI=0.059).

The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved. It helps to avoid local optimums and to find faster the global one. The theory of intuitionistic fuzzy logic, which is the basis of InterCriteria analysis (ICrA), is used to study the proposed hybrid algorithms for ant colony optimization (ACO). Different algorithms for ICrA implementation are applied on the results obtained by hybrid ACO algorithms for Multiple Knapsack Problem. The hybrid algorithms behavior is compared to the traditional ACO algorithm. Based on the obtained numerical results from the algorithms performance and from the ICrA the efficiency and effectiveness of the proposed hybrid ACO algorithms, combined with appropriate local search procedure, are confirmed.KeywordsLocal searchAnt colony optimizationHybrid algorithmIntercriteria analysis algorithmsKnapsack problemIndex matrixIntuitionistic fuzzy sets

A hybrid approach to couple the discrete velocity method and Method of Moments for rarefied gas flows

Summary In the process of waterflood development in oil and gas reservoirs, the injection/production ratio is one of the key indicators affecting both the development effectiveness and economic benefits. A reasonable injection/production ratio directly impacts the development parameters, such as reservoir pressure maintenance levels and water-cut rise rates, and it helps maintain high production capacity in the oil field, ultimately achieving a higher recovery factor. However, existing constrained-waterflood reservoir production optimization methods often face challenges such as time-consuming calculations and the risk of getting trapped in local optima when dealing with such nonlinear constraint problems, making it difficult to effectively achieve a balance between constraints and economic benefits. To address this issue, we propose an injection/production ratio–constrained production optimization method based on the worst-case soft actor-critic (WCSAC) algorithm. This method effectively handles the constraint issue by introducing a safety critic, ensuring the injection/production ratio constraint is met, and maximizing economic benefits throughout the entire life cycle. Specifically, this method models the injection/production ratio–constrained production optimization problem as a constrained Markov decision process (CMDP). On the one hand, it uses conditional value at risk (CVaR) as a safety metric to assess the safety risks of the policy and achieves a balance between reward and safety by adjusting adaptive safety weights. On the other hand, it uses the maximum entropy mechanism to enhance policy exploration and improve the efficiency of global optimization. This results in the training of a stable and reliable safe reinforcement learning (RL) agent to achieve injection/production ratio–constrained production optimization. The agent can adjust and determine the production optimization plan that satisfies the injection/production ratio constraint in real time based on the current reservoir development status, without the need for retraining, thus reducing computational time. To validate the performance of the proposed method, tests were conducted in two reservoir models. The analysis results indicate that, compared with other optimization methods, the proposed method can achieve a higher net present value (NPV) while satisfying the injection/production ratio constraints, demonstrating good model applicability.

The hybrid finite element/boundary element (FE/BE) computational scheme is advantageous in that it satisfies boundary conditions at infinity while avoiding meshing and computations in the air regions. Of numerous hybrid schemes, one using the fundamental solution of Poisson's equation as the weighting function has been employed successfully in many fields. Analyses in electromagnetic (EM) launch usually involve strong coupling among EM, thermal, and structural equations. The hybrid method has the added benefit that it may be fashioned to incorporate the effect of structural deformations on EM fields. Here, a three-dimensional (3D) hybrid algorithm using the fundamental solution of Poisson's equation as the weighting function is investigated and implemented in the parallel version of the code electromechanical analysis program in 3D (EMAP3D). The method is validated using two examples with semianalytical solutions: a 3D ring conductor with rectangular cross section carrying uniform current, and a quadrupole configuration with four independent, closed-ring conductors excited by imposed potential differences. The solutions obtained using the hybrid method match the semianalytical solutions within approximately four percent deviations.

College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm

The continuous defensive location problem (CDLP) is an NP-hard problem well investigated in the fields of competitive facility location. CDLP is a bi-level programming problem, where a decision maker locates defensive facilities with different capacities in the vertices of the network in order to avoid her/his aggressors from reaching core which is an important vertex in the network. In the present research, a hybrid method combining the imperialist competitive algorithm (ICA) and BFGS algorithm is presented to solve the CDLP. The proposed hybrid method integrates the ICA and the BFGS algorithm, providing a highly near-optimal solution. The upper-level problem solves the optimal location of defense facilities, and hybrid algorithm is applied. The lower-level problem is the shortest path problem which is solved by the Dijkstra method. The feasibility of the proposed hybrid method is demonstrated for a number of small, medium and large instances of the problem. The test results are compared with those obtained by genetic algorithm, particle swarm optimization and ICA in terms of solution accuracy and required CPU time. Simulation results reveal that the proposed hybrid method is feasible, robust and more effective in solving the CDLP than conventional metaheuristic methods.

The main aim of this work was to perform rigid registration of Computed Tomography (CT) and scanner datasets. The surgeon applies CT and scanner datasets in computer aided surgery and performs registration in order to visualize the location of surgical instrument on screen. It is well known fact that the registration procedure is crucial for efficient computer aiding of surgery. Selected algorithm should take into account types of datasets, required accuracy and time of calculations. The algorithms are classified basing on the various criteria: e.g. precision (coarse and fine registration), types of pointset (set of pair of corresponding points – so called point-point method, unorganized sets of points – so called surface registration). The paper presents exemplary results of applying the following algorithms: Landmark Transform (point-point registration), two methods of uninitialized Iterative Closest Point type (surface registration) and a hybrid method. The evaluated factors were: distance error (mean, minimal and maximal value) and running time of algorithm. The algorithms were tested on various datasets: (1) two similar datasets from Computed Tomography (one is geometrically transformed), (2) Computed Tomography dataset and cloud of points recorded using 3D Artec Space Spider scanner. In the first case the mean error values equaled: 102.08 mm – 121.70 mm for uninitialized ICPs methods, 0.005 mm for Landmark Transform method, and 0.0003 mm for hybrid method. The slowest algorithms in our tests were ICPs methods, faster was hybrid algorithm, and the fastest was Landmark Transform method. In the second case the distance errors were evaluated in four selected points, and the smallest errors were: 23.21 mm for uninitialized ICPs method, 0.69 mm for Landmark Transform, 9.03 for hybrid method. All algorithms were relatively slow for these large datasets, the fastest was Landmark Transform. In the second part of research we analysed the Target Registration Error (TRE) for fused Computed Tomography and scanner-recorded dataset. The TRE values equaled 0.7 mm - 2.8 mm. The results of CT – scanner datasets registration highly depend on the similarity of sets, especially their overlapping, but also their resolutions and uniformities.

[EN] Despite the importance of infrastructure in economic and social development, available funds for their maintenance are often insufficient, leading to a rapid deterioration. In this context arises the discipline of infrastructure asset management. By combining engineering principles with business practices and economic theory, infrastructure asset management enables an optimal resource allocation for the operation of infrastructure over its lifecycle.\nTraditionally, the evaluation of maintenance alternatives has been focused in economic and technical terms. Recent efforts have been made to quantify the environmental impact of these activities. However, current models lack of an integrated evaluation. This limitation creates the need to develop a sustainable evaluation of maintenance alternatives that integer technical, economic and environmental aspects over the infrastructure life-cycle.\nWith respect to the allocation of maintenance funds, optimization methods mainly used are mathematical programming and near-optimization methods. Regarding near-optimization methods, too little attention has been paid to heuristic algorithms, which so far have only been applied to solve the problem at the project level. In addition, optimization applications in infrastructure management are mainly focused on one objective, ignoring thus the complex and multi-objective nature of the real problem. There is an opportunity to develop a heuristic multi-objective optimization tool that, considering a sustainable evaluation of alternatives, improves the current allocation of maintenance resources.\nIn view of this background, the main objective of this research is to develop a tool for the sustainable evaluation of maintenance alternatives and the multi-objective heuristic optimization. This tool enables a more sustainable and efficient allocation of available resources for the maintenance of infrastructure at the network level. This tool is applied to a case study dealing with the management of an urban pavement network in Chile.\nThis research concludes that the heuristic algorithms based on local search heuristics are inefficient to solve the budget allocation problem. Given this limitation, a new hybrid method is developed. It combines GRASP (Greedy Randomized Adaptative Search Procedure), GLS (Guided Local Search) and GFB (Greedy First Best) algorithms. Moreover, it evaluates the maintenance alternatives considering, in an integrated manner, technical, economic and environmental aspects.\nThe proposed hybrid algorithm results in maintenance programs with an average effectiveness that is 9% higher than the one obtained with local search heuristics. The hybrid algorithm also requires in this process a lower computational effort. The application of the optimization tool to the Chilean case study improves the current management of urban pavements. The average network condition is 22% higher than the one obtained under the current practices and the generated CO2 emissions are reduced in 12%.\nIn practical terms, optimal programs consider a proactive maintenance policy. This policy applies preservation treatments when the pavement condition is still good. Finally, the optimization tool produces in this research enables a better planning of maintenance funds over time. Based on the evidence demonstrated by the Chilean case study, it is concluded that the funding level over time is a key factor in the technical and environmental performance of the network.