Search Step Research Articles

A search step optimization in an ambiguity function-based GNSS precise positioning

The search procedure, as a part of the Modified Ambiguity Function Approach (MAFA), is conducted in the coordinate space. The main advantage of searching for a fixed solution in the coordinate domain, instead of in the ambiguity domain, is the constant search space dimension, which amounts to three. In contrast, an ambiguity space dimension can presently achieve over twenty when the positioning is based on multi-system data. Thus, in the MAFA method, the computational complexity is independent of the number of satellites. We propose a new method of estimating the length of the search step. In this method, the actual satellite configuration determines the size of the search step. Therefore, the data-driven search step is always optimal, regardless of the current satellite configuration. The mathematical model of the new approach is provided together with a detailed algorithm. The numerical experiment follows the description of the search procedure.

A memetic animal migration optimizer for multimodal optimization

Unimodal optimization algorithms can find only one global optimum solution, while multimodal ones have the ability to detect all/most existing local/global optima in the problem space. Many practical scientific and engineering optimization problems have multiple optima to be located. There are a considerable number of optimization approaches in the literature to address the unimodal problems. Although multimodal optimization methods have not been studied as much as the unimodal ones, they have attracted an enormous amount of attention recently. However, most of them suffer from a common niching parameter problem. The main difficulty faced by existing approaches is determining the proper niching radius. Determining the appropriate radius of the niche requires prior knowledge of the problem space. This paper proposes a novel multimodal optimization scheme that does not face the dilemma of having prior knowledge of the problem space as it does not require the niching parameter to be determined in advance. This scheme is the extended version of the unimodal animal migration optimization (AMO) algorithm that has the capability of taking advantage of finding multiple solutions. Like other multimodal optimization approaches, the proposed MAMO requires specific modifications to make it possible to locate multiple optima. The local neighborhood policy is modified to adapt the multimodal search by utilizing Coulomb's law. Also, Coulomb's law is also applied to decide the movement direction of the individuals. Hence, instead of moving an individual toward the two randomly chosen individuals, it moves toward the near and good enough two neighborhoods. Additionally, a further local search step is performed to improve the exploitation. To investigate the performance of the MAMO, the comparisons are conducted with five existing multi-modal optimization algorithms on nine benchmarks of the CEC 2013 competition. The experimental results reveal that the MAMO performs success in locating all or most of the local/global optima and outperforms other compared methods. Note that the source codes of the proposed MAMO algorithm are publicly available at www.taymaz.dev .

3D segmentation of lungs with juxta-pleural tumor using the improved active shape model approach.

BACKGROUND AND OBJECTIVE: At present, there are many methods for pathological lung segmentation. However, there are still two unresolved problems. (1) The search steps in traditional ASM is a least square optimization method, which is sensitive to outlier marker points, and it makes the profile update to the transition area in the middle of normal lung tissue and tumor rather than a true lung contour. (2) If the noise images exist in the training dataset, the corrected shape model cannot be constructed.METHODS: To solve the first problem, we proposed a new ASM algorithm. Firstly, we detected these outlier marker points by a distance method, and then the different searching functions to the abnormal and normal marker points are applied. To solve the second problem, robust principal component analysis (RPCA) of low rank theory can remove noise, so the proposed method combines RPCA instead of PCA with ASM to solve this problem. Low rank decompose for marker points matrix of training dataset and covariance matrix of PCA will be done before segmentation using ASM.RESULTS: Using the proposed method to segment 122 lung images with juxta-pleural tumors of EMPIRE10 database, got the overlap rate with the gold standard as 94.5%. While the accuracy of ASM based on PCA is only 69.5%.CONCLUSIONS: The results showed that when the noise sample is contained in the training sample set, a good segmentation result for the lungs with juxta-pleural tumors can be obtained by the ASM based on RPCA.

Optimal allocation of higher education resources based on fuzzy particle swarm optimization

The development concept of higher education will have a direct impact on the development direction of higher education, because the concept usually reflects people's understanding of the law of higher education, and the current education concept restricts the scale, quality, structure and efficiency of higher education to a certain extent. Throughout the evolution of the world's higher education development concept, it has generally experienced the overall coordination of the economic growth, the internal and external laws, and the people-centered sustainable development. This paper hopes to provide effective help by joint intelligent algorithms. In this paper, the particle swarm optimization algorithm in intelligent optimization algorithm and the FCM algorithm in cluster analysis are studied in depth, and the two are merged together to propose a fuzzy clustering algorithm based on improved particle swarm optimization algorithm. The main research contents of this thesis include: Firstly, the PSO algorithm is improved, and the random search step levy flights and PSO algorithm are repeatedly called by the iterative mode by introducing the eagle strategy. After improvement, the ESPSO algorithm can achieve a balance between global search and local development, and make the particles have more satisfactory search ability and convergence effect. Then the improved PSO algorithm is integrated into the FCM algorithm to solve the above problems of the FCM clustering algorithm and improve the clustering effect. The experimental results show that the improved espso algorithm and FCM algorithm are suitable for actual data. The simulation results show that the algorithm has better clustering effect than the traditional clustering algorithm.

Combining the cross-entropy algorithm and ∈-constraint method for multiobjective optimization

Abstract This paper aims to propose a new hybrid approach for solving multiobjective optimization problems. This approach is based on a combination of global and local search procedures. The cross-entropy method is used as a stochastic model-based method to solve the multiobjective optimization problem and reach a first elite set of global solutions. In the local search step, an ∈-constraint method converts the multiobjective optimization problem to a series of parameterized single-objective optimization problems. Then, sequential quadratic programming (SQP) is used to solve the derived single-objective optimization problems allowing to reinforce and improve the global results. Numerical examples are used to demonstrate the efficiency and effectiveness of the proposed approach.

Multifidelity Genetic Transfer: An Efficient Framework for Production Optimization

Summary Production optimization led by computing intelligence can greatly improve oilfield economic effectiveness. However, it is confronted with huge computational challenge because of the expensive black-box objective function and the high-dimensional design variables. Many low-fidelity methods based on simplified physical models or data-driven models have been proposed to reduce evaluation costs. These methods can approximate the global fitness landscape to a certain extent, but it is difficult to ensure accuracy and correlation in local areas. Multifidelity methods have been proposed to balance the advantages of the two, but most of the current methods rely on complex computational models. Through a simple but efficient shortcut, our work aims to establish a novel production-optimization framework using genetic transfer learning to accelerate convergence and improve the quality of optimal solution using results from different fidelities. Net present value (NPV) is a widely used standard to comprehensively evaluate the economic value of a strategy in production optimization. On the basis of NPV, we first established a multifidelity optimization model that can synthesize the reference information from high-fidelity tasks and the approximate results from low-fidelity tasks. Then, we introduce the concept of relative fidelity as an indicator for quantifying the dynamic reliability of low-fidelity methods, and further propose a two-mode multifidelity genetic transfer learning framework that balances computing resources for tasks with different fidelity levels. The multitasking mode takes the elite solution as the transfer medium and forms a closed-loop feedback system through the information exchange between low- and high-fidelity tasks in parallel. Sequential transfer mode, a one-way algorithm, transfers the elite solutions archived in the previous mode as the population to high-fidelity domain for further optimization. This framework is suitable for population-based optimization algorithms with variable search direction and step size. The core work of this paper is to realize the framework by means of differential evolution (DE), for which we propose the multifidelity transfer differential evolution (MTDE). Corresponding to multitasking and sequential transfer in the framework, MTDE includes two modes, transfer based on base vector (b-transfer) and transfer based on population (p-transfer). The b-transfer mode incorporates the unique advantages of DE into fidelity switching, whereas the p-transfer mode adaptively conducts population for further high-fidelity local search. Finally, the production-optimization performance of MTDE is validated with the egg model and two real field cases, in which the black-oil and streamline models are used to obtain high- and low-fidelity results, respectively. We also compared the convergence curves and optimization results with the single-fidelity method and the greedy multifidelity method. The results show that the proposed algorithm has a faster convergence rate and a higher-quality well-control strategy. The adaptive capacity of p-transfer is also demonstrated in three distinct cases. At the end of the paper, we discuss the generalization potential of the proposed framework.

An improved grid search algorithm to optimize SVR for prediction

Parameter optimization is an important step for support vector regression (SVR), since its prediction performance greatly depends on values of the related parameters. To solve the shortcomings of traditional grid search algorithms such as too many invalid search ranges and sensitivity to search step, an improved grid search algorithm is proposed to optimize SVR for prediction. The improved grid search (IGS) algorithm is used to optimize the penalty parameter and kernel function parameter of SVR by automatically changing the search range and step for several times, and then SVR is trained for the optimal solution. The available of the method is proved by predicting the values of soil and plant analyzer development (SPAD) in rice leaves. To predict SPAD values more quickly and accurately, some dimension reduction methods such as stepwise multiple linear regressions (SMLR) and principal component analysis (PCA) are processed the training data, and the results show that the nonlinear fitting and prediction performance of accuracy of SMLR-IGS-SVR and PCA-IGS-SVR are better than those of IGS-SVR.

A Polar Coordinate Perspective on Motion Search in Video Coding

Video coding heavily relies on motion compensated predictive coding to achieve its compression efficiency. As a central component, the block matching motion search exploits the tradeoff between encoder complexity and compression efficiency. The diamond search and its variants are widely used in video encoders. They typically start with the largest search step size and scale down the step size by half at each stage thereafter. To cover an N×N pixel search region, the diamond search class requires a search complexity of O(logN) check points. In this work, we view the motion search as a sampling problem around the initial reference point in polar coordinates. We modify the sampling pattern at each search stage to better approximate a circle. The search radius is scaled down by √2 between stages, in contrast to the factor of 2 used by the diamond search class, such that the sampling density is increased by a factor of 2 between stages. It retains a search complexity of O(logN) over an N×N pixel region, with a linear increment as compared to the diamond search. It is experimentally shown that the proposed search pattern provides compression gains at the expense of a modest increase in encoder complexity.

Structural Damage Identification Based on Improved Fruit Fly Optimization Algorithm

To locate the damage of the structure efficiently and judge the damage degree, this paper proposes an improved fruit fly optimization algorithm (IFOA). Aiming at the problem of poor convergence of the standard fruit fly optimization algorithm in the face of complex structure damage identification, the IFOA introduces the concept of collaborative search of two subpopulations. The IFOA divides the entire population into positive subgroups and negative subgroups based on the individual taste concentration results. Among them, the positive subgroup uses the improved dynamic adaptive search step size to perform a fine search locally to improve its local search ability. Negative subgroups continue to use the standard fruit fly optimization algorithm for optimization, taking advantage of the powerful global search capabilities of the standard fruit fly optimization algorithm. It enables the algorithm to balance global and local search capabilities, prevents the algorithm from falling into local optimum, and speeds up the convergence speed and accuracy of the algorithm. Simulation results show that IFOA can effectively identify the damage location and damage degree of the structure, and it still performs well when facing the complex steel truss damage identification.

SoC-Based Droop Coefficients Stability Region Analysis of the Battery for Stand-Alone Supply Systems With Constant Power Loads

The droop control is an advantageous approach for stand-alone supply systems consisting of multiple batteries, allowing among various inverters without intercommunication. The droop coefficients of batteries always vary with their state-of-charge (SoC) and charge/discharge mode, resulting in small-signal instability. Nevertheless, the existing impedance-based approaches can only assess the droop coefficients stability point, but not the stability region. Therefore, this article proposes a droop coefficients stability region analysis approach. First, the charge/discharge SoC-based droop controlled battery, the P&Q controlled distributed generator and the constant power load are separately discussed. Meanwhile, the state matrix and return-ratio matrix are established, respectively. Furthermore, the novel forbidden region criterion based on the return-ratio matrix is constructed, which reduces conservatism compared with norm-based impedance criteria and partial forbidden region criteria. Such a forbidden region criterion is first switched to the Hurwitz identification problem regarding the equivalent return-ratio matrix. Combined the state matrix and the equivalent return-ratio matrix, the generalized incidence matrix is constructed to simultaneously identify subsystem stability and interactive stability. Based on the generalized incidence matrix, an adaptive step search strategy is proposed to obtain the droop coefficients coordinated stability region. Finally, the simulation and experimental results illustrate the validity of the proposed method.

Just-in-time scheduling for a distributed concrete precast flow shop system

This paper focuses on the distributed concrete precast flow shop scheduling problem to minimize total weighted earliness and tardiness. It is crucial for a precast manufacturer to schedule jobs effectively to meet shipping dates because of tight due date, limited inventory capacity and huge tardiness penalties. In order to respond quickly to customer demands, many concrete precast manufacturers have adopted the distributed-multiple-factory mode in which job assignment and scheduling have to be decided jointly. To solve this problem, through analyzing the characteristics of distributed precast production, we first develop a novel mixed integer nonlinear programming (MINLP) model and then transform it into an effective mixed integer linear programming (MILP) model by linearization techniques. Additionally, since the problem is more complex than the classical distributed flow shop scheduling problem, which is NP-hard in most cases, we propose a hybrid iterated greedy algorithm (HIG) by integrating due date related NEH-based heuristics, problem-specific knowledge, different local search methods, and mechanism of destruction and reconstruction. Subsequently, we propose a hybrid tabu search and iterated greedy (HTS_IG) in which a hybrid tabu search (HTS) is run at first and then an iterated greedy (IG) starts from the best solution obtained by HTS. Aimed at improving search efficiency, some structural properties of the schedules are explored and integrated into the local search steps of HIG and HTS_IG. We also develop a hybrid genetic algorithm and variable neighborhood search (HGA_VNS) and a two-phase heuristic method (TPHM) for comparison. Finally, extensive experiments and deep analysis are conducted on instances with different combination of problem parameters. Computational results show the effectiveness of the MILP model and the proposed algorithms. The computational analysis indicates that, on average, HTS_IG performs best among all the proposed metaheuristics. The effectivenesses of local search based on problem-specific knowledge (PSK) in HIG and HTS_IG are also verified.

Интеграция методов поиска в ширину и логического вывода для удовлетворения табличных ограничений

Within the Constraint Programming technology, so-called table constraints such as typical tables, compressed tables, smart tables, segmented tables, etc, are widely used. They can be used to represent any other types of constraints, and algorithms of the table constraint propagation (logical inference on constraints) allow eliminating a lot of "redundant" values from the domains of variables, while having low computational complexity. In the previous studies, the author proposed to divide smart tables into structures of C- and D-types. The generally accepted methodology for solving constraint satisfaction problems is the combined application of constraint propagation methods and backtracking depth-first search methods. In the study, it is proposed to integrate breadth-first search methods and author`s method of table constraint propagation. D-type smart tables are proposed to be represented as a join of several orthogonalized C-type smart tables. The search step is to select a pair of C-type smart tables to be joined and then propagate the restrictions. To determine the order of joining orthogonalized smart tables at each step of the search, a specialized heuristic is used, which reduces the search space, taking into account further calculations. When the restrictions are extended, the acceleration of the computation process is achieved by applying the developed reduction rules for the case of C-type smart tables. The developed hybrid method allows one to find all solutions to the problems of satisfying constraints modeled using one or several D-type smart tables, without decomposing tabular constraints into elementary tuples.

A Distributed Second-Order Augmented Lagrangian Method for Distributed Model Predictive Control

Abstract In this paper we present a distributed second-order augmented Lagrangian method for distributed model predictive control. We distribute the computations for search direction, step size, and termination criteria over what is known as the clique tree of the problem and calculate each of them using message passing. The algorithm converges to its centralized counterpart and it requires fewer communications between sub-systems as compared to algorithms such as the alternating direction method of multipliers. Results from a simulation study confirm the efficiency of the framework.

A Novel Sliding-Discrete-Control-Set Modulated Model Predictive Control for Modular Multilevel Converter

Model predictive control (MPC) method has been recognized as one of the most promising technologies for the modular multilevel converter (MMC) due to the fast dynamic response and its simple realization. However, conventional finite control set (FCS) MPC methods for MMC are facing some challenges, such as high computation burden, poor steady-state performance, dependence on weighting factors, and variable switching frequency. In order to solve these problems, a novel sliding-discrete-control-set (SDCS) modulated MPC (MMPC) is proposed for MMC in this paper. Based on the adaptive search step in the output current control, only three control sets are evaluated in each period. In addition, the independent circulating current controller is applied in the proposed MMPC method. With the circulating current controller, the circulating currents are well regulated, and the arm capacitor voltage balancing is realized by circulating current injection. As a result, there is no weighting factor involved in the proposed MMPC method. Compared with the conventional MPC methods, the proposed method obtains a fixed switching frequency in each submodule (SM) and a low comparable computation burden. Simulation and experimental results verify the effectiveness of the proposed method.

Fuzzy based Search in Motion Estimation for Real Time Video Compression

Video compression ratio, quality and efficiency are determined by the motion estimation algorithm. Motion estimation is used to perform inter frame prediction in video sequences. The individual frames are divided into blocks the motion estimation is computed by a video codec such as H.264. A video codec computes the displacement of block between the previous frame (reference frame) and the current frame, for each block in current frame the best motion vector is determined in the reference frame as a block belongs to a current frame. In this, research paper, a novel technique has been presented for motion vector calculation, using fuzzy Gaussian membership function. The motion estimation block uses fuzzy membership function to estimate the connectedness of different blocks of the current frame to that of the reference frame The fuzzy decision matching is done based on the matching criterion and the best matching block is selected. The motion vectors are thus calculated with respect to the reference frame. The fuzzification process produces optimally matched blocks, which are then utilized to calculate the motion vectors of the predicted frame. Using fuzzy based search the search area is automatically updated and adaptive search steps provides an optimized result of search. As in real time streaming no file is exchanged during the transmission user is not able to download the file the only way for smooth transmission is frame management fuzzy based search for the motion estimation provides a better compression for the predicted frames.

An ils-based heuristic applied to the car renter salesman problem

The present paper tackles the Car Renter Salesman Problem (CaRS), which is a Traveling Salesman Problem variant. In CaRS, the goal is to travel through a set of cities using rented vehicles at minimum cost. The main aim of the current problem is to establish an optimal route using rented vehicles of different types to each trip. Since CaRS is N P-Hard, we herein present a heuristic approach to tackle it. The approach is based on a Multi-Start Iterated Local Search metaheuristic, where the local search step is based on the Random Variable Neighborhood Descent methodology. An Integer Linear Programming Formulation based on a Quadratic Formulation from literature is also proposed in the current study. Computational results for the proposed heuristic method in euclidean instances outperform current state-of-the-art results. The proposed formulation also has stronger bounds and relaxation when compared to others from literature.

An Improved Artificial Bee Colony Algorithm for Community Detection in Bipartite Networks

In the past few decades, although people have conducted in-depth research on community detection in one-mode networks, community detection in bipartite networks has not been extensively researched. In this paper, we propose an improved artificial bee colony algorithm named IABC-BN, which is used to detect the communities in two-mode graphs (i.e. bipartite graphs) with two kinds of vertices in the cluster (i.e. community). Firstly, this paper proposed a novel population initialization process of artificial bee colony (ABC) method for two-mode graph cluster identification. This initialization method can improve the diversity of initial population of ABC and speed up its convergence rate. Secondly, in the employed bee search step of the algorithm, a new combinatorial search equation is proposed. This equation is guided by the global optimal solution and the better neighbour solution of the current solution. By using this combination equation and the increased parameter perturbation frequency, the exploitation ability of the algorithm is further enhanced. Thirdly, in the onlooker bees step, another new combination search equation is also proposed. This equation improves the exploitation level of the algorithm, and an opposition based studying method is employed to promote the exploitation ability of the algorithm. Lastly, in scout bee stage, a probability threshold $\beta $ is introduced to enhance the exploration ability of the algorithm and improve the population diversity of the algorithm. To our knowledge, the IABC-BN method presented in this paper is the first ABC method used to cluster identification in two-mode graphs with two kinds of vertices in the cluster. For verifying the accuracy of the results of the proposed method, a large number of experiments are carried out making use of synthetic bipartite graphs and real bipartite graphs. The test outcomes show that this algorithm is an excellent algorithm for cluster discovery in two-mode graph.

Enhancement of Defect Detectability in Pneumatic Pressure Equipment Using an Automatic Detection Technique in ECPT

Abstract Pneumatic pressure equipment is an important component of wind tunnel test systems, which have been extensively applied in most modern industries. To guarantee the reliability and safety of pneumatic pressure equipment, a regular inspection is needed. To improve the inspections process, automating the process of identifying defects continues to receive attention in the research community. In this paper, in order to achieve automatic defect identification, an improved feature extraction algorithm in eddy current pulsed thermography is presented. The presented feature extraction algorithm contains four elements: data block selection, variable step search, relation value classification, and between-class distance decision function. The data block selection and variable step search are integrated to decrease the redundant computations in the automatic defect identification. The goal of the classification and between-class distance calculation is to select the typical features of a thermographic sequence. The main image information can be extracted by the method precisely and efficiently. Experimental results are provided to demonstrate the capabilities and benefits (i.e., increased precision, reduced processing time) of the proposed algorithm in automatic defect identification.

Path planning of mobile robot based on improved ant colony algorithm for logistics.

The path planning of robot is of great significance for the logistics industry, which helps to improve the efficiency of warehousing, sorting and distribution. On the basis of ant colony algorithm, multi step search strategy is used instead of single step search strategy, pheromone update mechanism is redesigned, and path smoothing is configured to improve the performance of the algorithm. The experimental results show that the improved ant colony algorithm proposed in this paper can plan a shorter optimal path on the 16 * 16 grid logistics storage site, and the path length is saved by 9.21%.

Displacement history and potential triggering factors of Baige landslides, China revealed by optical imagery time series

In this study, on the basis of the image correlation technique and the time-series images of Landsat-8 (L8), Sentinel-2 (S2), and GaoFen-2 (GF2), a systematic technical process is designed to investigate the precursory displacement evolution of two successive slope failures occurred in Baige Village, China on Oct. 11, 2018 and Nov. 3, 2018. An innovative fusion strategy is proposed to investigate the displacement history of the Oct. 11, 2018 Baige landslide, which has two steps: (1) selecting the optimal correlation window size and search step to eliminate the inconsistency of image correlation for different sensors, and (2) inverting the fused displacement time-series from correlation results to enhance the temporal sampling density. The normalized displacement velocity indicates that the Oct. 11, 2018 Baige landslide is characterized by high-speed sliding (28 m/yr) and high shear outlet with an average elevation difference of around 82 m. The fused displacement time series indicates that the whole phase transformation can be from the secondary stage to the tertiary stage on Mar. 26, 2017. Furthermore, the displacement velocity shows four quiescence phases in the secondary stage and two acceleration phases in the tertiary stage. The seasonal precipitation is assumed as the main external triggering factor, and it combined with the brittle geological material attributes to control the precursory landslide displacement evolution and caused the catastrophic slope failure on Oct. 11, 2018. The precursory displacement signals with a magnitude above 5 m/day of the second landslide (Nov. 3, 2018) is quantified by the S2 image correlation. This study highlights the prospects of optical observation time series with medium-/high-resolution in detecting and quantifying the spatio-temporal evolution characteristics of long-term creeping landslides which may play a significant role in the early warning of the catastrophic slope failure.