Local Search Method Research Articles

Heterogeneity constrained color ellipsoid prior image dehazing algorithm

To address the issue of inaccurate transmission estimation in areas with sudden depth changes in hazy images, the image dehazing algorithm with Heterogeneity Constrained Color Ellipsoid Prior (HC-CEP) is proposed. Firstly, a local threshold optimization quadtree search method is designed, in order to solve the problem of inaccurate global atmospheric light estimation and avoid the overall darkening of the image caused by white objects.Then, different regions are selected by a local block heterogeneity window to construct diversity prior vectors to estimate the initial transmission. Finally, by constructing neighborhood heterogeneity weighted constraints on each pixel of the initial transmission and improving the high-order differential filtering of the Scharr operator, the optimization of the transmission is achieved, eliminating halo artifacts in the depth mutation area of the image. Both qualitative and quantitative experimental results show that the proposed algorithm comprehensively considers the heterogeneity of local pixel characteristics, and the dehazing image obtained has better color fidelity and edge detail preservation ability, which can effectively eliminate halo artifacts on the edges of hazy images.The proposed method has better dehazing performance and has a 8% improvement in terms of the FADE metric compared to state-of-the-art dehazing methods.

Exploring the Molecular Docking Interactions between the Polyherbal Formulation Ibadhychooranam and Human Aldose Reductase Enzyme as a Novel Approach for Investigating its Potential Efficacy in Management of Cataract

Background: Kannir sirandha urupillai... which means no organ is better than eye. Eye health and treatment have held significant importance in the Siddha system of medicine since ancient times, emphasizing the holistic approach to well-being encompassing physical, mental, and spiritual aspects. Eye disease is classified into 96 types and their ethology, treatment method, preventive measures all are explained in many of the Siddha literature. Among them cataract is one of the leading cause of blindness which is characterised by clouding of eye’s natural lens, leading to blurry, foggy or flimy vision. This paper will elaborates about the docking analysis of poly herbal formulation Ibadhy chooranam against Human aldose reductase enzyme for cataract.  Aim & Objective: The aim of this study is to investigate the potential efficacy of the polyherbal formulation IbadhyChooranam in preventing or treating cataracts through molecular docking analysis of its interactions with the human aldose reductase enzyme.  Methodology: Docking simulations were conducted for the extracted phytoconstituents of IbadhyChooranam against the Human Aldose Reductase Enzyme. AutoDock tools were utilized to incorporate hydrogen atoms, Coleman united atom type charges, and solvation parameters. The docking process employed the Lamarckian genetic algorithm along with the Solis & Wets Local Search method to simulate ligand-receptor interactions.  Result: The current study revealed that phytochemicals present in IbadhyChooranam, including Nerolidol, Ellagic acid, Phyllanthin, Costunolide, Embelin, Cyperolone, Zingiberene, Piperic acid, Piperine, and Lupeol, exhibited between 6 to 9 significant interactions with residual amino acids in the aldose reductase enzyme. In comparison, the standard drug Epalrestat demonstrated 8 viable interactions with the residual amino acids of the aldose reductase enzyme.  Conclusion: According to the computational analysis findings, it can be inferred that the bioactive compounds present in IbadhyChooranam exhibit notable binding affinity towards the target aldose reductase enzyme. Ibadhy chooranam inhibit the function of aldose reductase enzyme which delay the sorbitol accumulation and subsequent cataract development. Through this study, it was also justified that the Ibadhy chooranam possess significant anti-cataract activity.

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

The meaningful problem of improving crane safety, reliability, and efficiency is extensively studied in the literature and targeted via various model-based control approaches. In recent years, crane data-driven modeling has attracted much attention compared to physics-based models, particularly due to its potential in real-time crane control applications, specifically in model predictive control. This paper proposes grammar-guided genetic programming with sparse regression (G3P-SR) to identify the nonlinear dynamics of an underactuated crane system. G3P-SR uses grammars to bias the search space and produces a fixed number of candidate model terms, while a local search method based on an l0-regularized regression results in a sparse solution, thereby also reducing model complexity as well as reducing the probability of overfitting. Identification is performed on experimental data obtained from a laboratory-scale overhead crane. The proposed method is compared with multi-gene genetic programming (MGGP), NARX neural network, and Takagi-Sugeno fuzzy (TSF) ARX models in terms of model complexity, prediction accuracy, and sensitivity. The G3P-SR algorithm evolved a model with a maximum mean square error (MSE) of crane velocity and sway prediction of 1.1860 × 10−4 and 4.8531 × 10−4, respectively, in simulations for different testing data sets, showing better accuracy than the TSF ARX and MGGP models. Only the NARX neural network model with velocity and sway maximum MSEs of 1.4595 × 10−4 and 4.8571 × 10−4 achieves a similar accuracy or an even better one in some testing scenarios, but at the cost of increasing the total number of parameters to be estimated by over 300% and the number of output lags compared to the G3P-SR model. Moreover, the G3P-SR model is proven to be notably less sensitive, exhibiting the least deviation from the nominal trajectory for deviations in the payload mass by approximately a factor of 10.

A parallel deep adaptive large neighbourhood search algorithm for distributed heterogeneous hybrid flow shops with mixed-model assembly scheduling

ABSTRACT Nowadays, manufacturing enterprises must have fast response and flexible production capabilities to meet personalized and diversified market demands. Mixed-model production and distributed production have become the preferred production methods for enterprises. This article studies a distributed heterogeneous hybrid flow shop scheduling problem with a mixed-model assembly line (DHHFSP-MMAL), which consists of manufacturing and assembly stages. The DHHFSP-MMAL is modelled by a mixed integer linear programming (MILP) model. Three constructive heuristics and a parallel deep adaptive large neighbourhood search (PDALNS) problem are presented. A constructive heuristic with a group strategy is employed to obtain an initial solution. Several deep destroy-and-repair operators are proposed where problem-specific greedy local search methods are applied. The PDALNS assigns weights to destroy-and-repair operators to guide the selection of operators. The parallel computing technique is introduced to increase the efficiency of training. The experiments demonstrate that the PDALNS algorithm is an efficient and effective algorithm for solving the DHHFSP-MMAL problem.

Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem

A multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy (MOCOA-ML) was proposed to solve the optimal power flow (OPF) problem. MOCOA-ML adds external archives with grid mechanism on the basis of elite non-dominated sorting. It can guarantee the diversity of the population while obtaining the Pareto solution set. When selecting elite coyotes, there is a greater probability to select the elite in sparse areas, which is conducive to the development of sparse areas. In addition, combined with Meta-Lamarckian learning strategy, based on four crossover operators (horizontal crossover operator, longitudinal crossover operator, elite crossover operator and direct crossover operator), the local search method is adaptively selected for optimization, and its convergence performance is improved. First, the simulation is carried out in 20 test functions, and compared with MODA, MOPSO, MOJAYA, NSGA-II, MOEA/D, MOAOS and MOTEO. The experimental results showed that MOCOA-ML achieved the best inverted generational distance value and the best hypervolume value in 11 and 13 test functions, respectively. Then, MOCOA-ML is used to solve the optimal power flow problem. Taking the fuel cost, power loss and total emissions as objective functions, the tests of two-objective and three-objective bechmark problems are carried out on IEEE 30-bus system and IEEE 57-bus system. The results are compared with MOPSO, MOGWO and MSSA algorithms. The experimental results of OPF demonstrate that MOCOA-ML can find competitive solutions and ranks first in six cases. It also shows that the proposed method has obtained a satisfactory uniform Pareto front.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

Approximate Local Search Methods for Multi-objective Sequencing Problem

In the setting of a single machine, this study suggests approximation local search strategies to identify approximate solutions to the multi-objective sequencing problem, where the problem is the total of the four objectives: total completion time ∑Cj j= 1,….,n, total lateness ∑Lj, maximum lateness Lmax and maximum earliness Emax. Descent Method (DM), Simulated Annealing (SA), and genetic algorithm (GA) are three approximate local search techniques that are computer-implemented Matlab programs. On the basis of the outcomes of computing tests, conclusions are modeled on the effectiveness of the local search techniques.

Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in automobile manufacturing supply chain

This paper presents a novel distributed assembly permutation flowshop scheduling problem (DAPFSP) based on practical problems in automobile production. Different from the existing research on DAPFSP, this study considers that each component of the final product is composed of more than one part. Components are processed in a set of identical components manufacturing factories and are assembled into products in the assembly factory. The integration of manufacturing processes is an important objective of Industry 4.0. For solving this problem with the minimum makespan criterion, we introduce a three-level representation and a novel initialization method. To enhance the search ability of the proposed algorithms, we design three local search methods and two restart procedures according to characteristics of the problem. Then, by incorporating the problem specific knowledge with the social spider optimization algorithm (SSO), we propose three SSO variants: the SSO with hybrid local search strategies (HSSO), the HSSO with restart procedures (HSSOR), and the HSSOR with self-adaptive selection probability (HSSORP). Finally, 810 extended instances based on the famous instances are used to test the proposed algorithms. In most cases, HSSOR performs the best, with an average comparison metric value of 0.158% across three termination conditions, while the average comparison metric value for the best comparison method is 2.446%, which is 15.481 times that of HSSOR. Numerical results demonstrate that the proposed algorithms can solve the problem efficiently.

D 3 PBO: D ynamic D omain D ecomposition-based P arallel B ayesian O ptimization for Large-scale Analog Circuit Sizing

Bayesian optimization (BO) is an efficient global optimization method for expensive black-box functions, but the expansion for high-dimensional problems and large sample budgets still remains a severe challenge. In order to extend BO for large-scale analog circuit synthesis, a novel computationally efficient parallel BO method, D 3 PBO, is proposed for high-dimensional problems in this work. We introduce the dynamic domain decomposition method based on maximum variance between clusters. The search space is decomposed into subdomains progressively to limit the maximal number of observations in each domain. The promising domain is explored by multi-trust region-based batch BO with the local Gaussian process (GP) model. As the domain decomposition progresses, the basin-shaped domain is identified using a GP-assisted quadratic regression method and exploited by the local search method BOBYQA to achieve a faster convergence rate. The time complexity of D 3 PBO is constant for each iteration. Experiments demonstrate that D 3 PBO obtains better results with significantly less runtime consumption compared to state-of-the-art methods. For the circuit optimization experiments, D 3 PBO achieves up to 10× runtime speedup compared to TuRBO with better solutions.

Structural differences of Cu-Pd clusters with three potential parameters

Parameters of potential function have a great influence on the structures of bimetallic clusters. Three potential parameters, i.e., parameter sets C, P/N, and M, are applied to study the structural difference of Cu-Pd clusters within 147 atoms. The structural optimization is performed by adaptive immune optimization algorithm with constructed inner cores and iterated local search method. Results showed that for parameters C and P/N the structural motifs of 55-atom and 147-atom Cu-Pd clusters are almost the same, but the atomic distribution is different. As for clusters with 75 and 98 atoms, there exist obvious differences in geometric configurations.

A Multi-Objective Learning Whale Optimization Algorithm for Open Vehicle Routing Problem with Two-Dimensional Loading Constraints

With the rapid development of the sharing economy, the distribution in third-party logistics (3PL) can be modeled as a variant of the open vehicle routing problem (OVRP). However, very few papers have studied 3PL with loading constraints. In this work, a two-dimensional loading open vehicle routing problem with time windows (2L-OVRPTW) is described, and a multi-objective learning whale optimization algorithm (MLWOA) is proposed to solve it. As the 2L-OVRPTW is integrated by the routing subproblem and the loading subproblem, the MLWOA is designed as a two-phase algorithm to deal with these subproblems. In the routing phase, the exploration mechanisms and learning strategy in the MLWOA are used to search the population globally. Then, a local search method based on four neighborhood operations is designed for the exploitation of the non-dominant solutions. In the loading phase, in order to avoid discarding non-dominant solutions due to loading failure, a skyline-based loading strategy with a scoring method is designed to reasonably adjust the loading scheme. From the simulation analysis of different instances, it can be seen that the MLWOA algorithm has an absolute advantage in comparison with the standard WOA and other heuristic algorithms, regardless of the running results at the scale of 25, 50, or 100 datasets.

Два метода локального поиска в планировании открытых горных работ

This paper aims to investigate ways of planning open pit mining using local search (genetic algorithms are not disscussed in the paper). Open pit mining is a complex and multifaceted problem that requires efficient planning to optimize mineral extraction. The Local search methods are a powerful optimization tool to search for local optima in the space of lage size. This paper reviews two of the most commonly used local search approaches for open pit mining planning.

Pareto-optimal front generation for the bi-objective JIT scheduling problems with a piecewise linear trade-off between objectives

This paper proposes a novel method of Pareto front generation from a set of piecewise linear trade-off curves typically encountered in bi-objective just-in-time (JIT) scheduling problems. We have considered the simultaneous minimization of total weighted earliness and tardiness (TWET) and total flowtime (TFT) objectives in a single-machine scheduling problem (SMSP) with distinct job due dates allowing inserted idle times in the schedules. An optimal timing algorithm (OTA) is presented to generate the trade-off curve between TWET and TFT for a given sequence of jobs. The proposed method of Pareto front generation generates a Pareto-optimal front constituted of both line segments and points. Further, we employ a simple local search method to generate sequences of jobs and their respective trade-off curves, which are trimmed and merged to generate the Pareto-optimal front using the proposed method. Computational results obtained using problem instances of different sizes reveal the efficiency of the proposed OTA and the Pareto front generation method over the state-of-the-art methodologies adopted from the literature.

Large neighborhood local search method with MIP techniques for large-scale machining scheduling with many constraints

This study addresses the problem of scheduling machining operations in a highly automated manufacturing environment while considering the work styles of workers. In actual manufacturing, many aspects of the operation must be considered, such as constraints related to the works to be machined in the machining schedule and the states of the workers. To derive good solutions for such a large-scale problem with many constraints within a realistic amount of computation time, we develop an optimization technique based on a mixed-integer programming (MIP)-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments on a problem based on actual machining requirements are performed to verify the validity of the proposed method.

A Quality Prediction and Parameter Optimization Approach for Turbine Blade Multistage Manufacturing

As core components of an aircraft engine, turbine blades are featured by complex curved surface with strict precision requirements in multistage manufacturing processes (MMPs). Hence, reliable quality prediction and parameter optimization strategy on MMPs have long been in great demand. However, most techniques focus on the precision improving of single-stage single-product manufacturing systems; these improvements are difficult to implement in MMPs with coordinate transformations among multiple machines. In this article, we propose a noninvasive feature-based quality prediction and parameter optimization framework for high-precision MMPs. In particular, rather than retrofitting sensors onto machinery after installation, this study presents an executable and noninvasive solution by using coordinate measuring machine (CMM) data as input features for prediction. Based on features of different stages of machining, a sequential multistep deep learning architecture is developed to predict the final product quality. A preheating optimization algorithm, modified from a local gradient search method, is used for the optimization of key initial machining features, which results are further improved by the L-BFGS method, thus increasing the final production quality. To estimate the effectiveness of the proposed framework, experiments are carried out on two series of aviation turbine blades with complex curved surfaces from the Wuxi Turbine Blade Company Ltd. China. CMM data at key stages (i.e., blade root milling and comprehensive accurate milling) are used as inputs to predict the final geometrical errors. Comparison results verify that the proposed framework achieves the smallest root mean square error at 0.030 mm and 0.032 mm for two blades, respectively.

Vehicle Routing Problem as a Solution for Determining Goods Delivery Routes PT. Kreasi Beton Nusa Persada

VRP distributions have had difficulty overcoming the problem of finding channels with minimal depots to locations that have different places with different total demand. The purpose of this study is to analyze the problem of transportation routes in the distribution of products obtained from the initial location of distribution to users. This type of research is qualitative research. This research was conducted at PT. Nusa Persada Concrete Creations. The Nearest Neighbor method is used to determine the distribution of routes. The Local Search method is carried out to evaluate and improve the distribution of routes carried out at the beginning with the Nearest Neighbors method. The data analysis process consists of several stages with the Nearest Neighbor method and the LocalSearch method. The results of the study, namely the Model Vehicle Routing Problem (VRP) applied in determining ready mix delivery routes at PT. Nusapersada Concrete Creation using nearest and local neighbor methods. Vehicle Routing Problem (VRP) models using nearest and local neighbor methods can be used applied in determining ready mix delivery routes to limited companies. Nusapersada Concrete Creations. This makes distance and time more effective, as well as more cost efficient. New routes generated This is a route improvement solution that PT. The application of the Nusapersada Concrete Creations model results in a new route that reduces the distance closer, faster completion time, and fuel cost savings for truck vehicles compared to the initial route. This makes distance and time more effective, as well as more cost efficient.

An iterated greedy heuristic for collaborative Human-UAV search of missing tourists

This paper investigates a problem of collaborative human-unmanned aerial vehicle (UAV) search for missing tourists. A mathematical model including detection stage and rescue stage is constructed to describe practical operation scenarios. The objective function is formulated using conditional probability theory by introducing the weighted probability that tourists being rescued at an allowable time limit. To solve this combinatorial optimization problem, an iterated greedy heuristic (IGH) is designed to search solution space. IGH starts with an initialization procedure based on problem-specific knowledge, then a neighborhood perturbation-based local search method is implemented on perturbed solution to intensify exploitation capability. Meanwhile, an acceptance criterion is presented to avoid premature convergence. Ultimately, Taguchi method is utilized to calibrate parameters of IGH for best performance. Comparison with other algorithms on objective function value and search performance validates the effectiveness of our proposed algorithm.

Multi-product disassembly line balancing optimization method for high disassembly profit and low energy consumption with noise pollution constraints

Remanufacturing attains much attention from both industrial and academic sectors due to its beneficial roles in energy conservation and environment protection, where disassembly is a crucial part. To reach the comprehensive sustainability and global optimization of disassembling multiple end-of-life products, this paper suggests a multi-objective multi-product disassembly line balancing problem with considering disassembly profit, energy consumption and noise pollution simultaneously. According to the natures of the problem under consideration, a multi-objective integer programming model is constructed. Its goals are to reach maximum disassembly profit and realize minimum energy consumption while observing noise pollution requirements and resource constraints. Accordingly, a multi-objective group teaching optimization algorithm is specially devised. In it, rank and crowding distance methods are employed to partition the population into two groups. Moreover, precedence preserving crossover and mutation methods are severally used on the two groups to realize the teacher phase. Furthermore, to achieve the student phase, an adaptive local search method is applied to refine solutions in an external archive, and thus its exploitation ability is enhanced. By executing contrast experiments between the devised approach and its powerful competitors on a set of real-world test instances, the experimental results validate that it has highly-adaptive and well-superior performance in tackling the problem of concern.

Surrogate-Assisted Multi-Objective Evolutionary Optimization With Pareto Front Model-Based Local Search Method.

Some local search methods have been incorporated into surrogate-assisted multi-objective evolutionary algorithms to accelerate the search toward the real Pareto front (PF). In this article, a PF model-based local search method is proposed to accelerate the exploration and exploitation of the PF. It first builds a predicted PF model with current nondominated solutions. Then, some sparse points in the predicted PF are selected to guide the search directions of the local search in order to promote the search of promising sparse areas. The approximation degree of the predicted and real PFs will influence the speed of the local search, while extreme points can significantly influence the shape of the PF. To accelerate the search progress, the optima of surrogate models are utilized to promote the progress of finding extreme points. The proposed local search method is incorporated into a surrogate-assisted multi-objective evolutionary algorithm. The proposed surrogate-assisted multi-objective evolutionary algorithm with the proposed local search method is tested with Zitzler-Deb-Thiele (ZDT), Deb-Thiele-Laummans-Zitzler (DTLZ), and MAF instances. The experimental results demonstrated the efficiency of the proposed local search method and the superiority of the proposed algorithm.

Minimizing the maximum tardiness for a permutation flow shop problem under the constraint of sequence independent setup time

In this work, we will study a permutation flow shop scheduling problem under the constraint of sequence independent setup time. In our case, each machine requires a certain setup time to process all the different jobs assigned to it. Hence, this setup time will be independent of sequence of jobs, but will depend only on the nature of machine. The optimization objective is to minimize the maximum tardiness criterion. To solve this optimization problem, an exact method, heuristics and metaheuristics are the three main resolution methods that we have used. The exact method is represented by the mixed integer linear programming (MILP) model. In terms of the second category of resolution methods, we have been focused on two methods, the first is a modified heuristic based on Johnson rule (HBJR) while the second is based on the Nawaz–Enscore–Ham (NEH) algorithm. Finally, three metaheuristics have been used, namely the iterated local search (ILS) method, the iterated greedy (IG) algorithm and the genetic algorithm (GA). Our numerical results indicate that for the problems with small size instances, the NEH heuristic outperforms HBJR approach, while for relatively large size instances, the developed IG algorithm gives best results than both other metaheuristics ILS and GA.