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The article deals with the hybrid Ant Colony Optimization algorithm and its application to the Multi-Depot Vehicle Routing Problem (MDVRP). The algorithm combines both probabilistic and exact techniques. The former implements the bio-inspired approach based on the behaviour of ants in the nature when searching for food together with simulated annealing principles. The latter complements the former. The algorithm explores the search space in a finite number of iterations. In each iteration, the deterministic local optimization process may be used to improve the current solution. Firstly, the key parts and features of the algorithm are presented, especially in connection with the exact optimization process. Next, the article deals with the results of experiments on MDVRP problems conducted to verify the quality of the algorithm; moreover, these results are compared to other state-of-the-art methods. As experiments, Cordreau’s benchmark instances were used. The experiments showed that the proposed algorithm overcomes the other methods as it has the smallest average error (the difference between the found solution and the best known solution) on the entire set of benchmark instances.

To solve the problems of convergence speed in the ant colony algorithm, an improved ant colony optimization algorithm is proposed for path planning of mobile robots in the environment that is expressed using the grid method. The pheromone diffusion and geometric local optimization are combined in the process of searching for the globally optimal path. The current path pheromone diffuses in the direction of the potential field force during the ant searching process, so ants tend to search for a higher fitness subspace, and the search space of the test pattern becomes smaller. The path that is first optimized using the ant colony algorithm is optimized using the geometric algorithm. The pheromones of the first optimal path and the second optimal path are simultaneously updated. The simulation results show that the improved ant colony optimization algorithm is notably effective.

Radical pair kinetics is determined by the coherent and incoherent spin dynamics of spin pair and spin-selective chemical reactions. In a previous paper, reaction control and nuclear spin state selection by designed radiofrequency (RF) magnetic resonance was proposed. Here, we present two novel types of reaction control calculated by the local optimization method. One is anisotropic reaction control and the other is coherent path control. In both cases, the weighting parameters for the target states play an important role in the optimizing of the RF field. In the anisotropic control of radical pairs, the weighting parameters play an important role in the selection of the sub-ensemble. In coherent control, one can set the parameters for the intermediate states, and it is possible to specify the path to reach a final state by adjusting the weighting parameters. The global optimization of the weighting parameters for coherent control has been studied. These manifest calculations show the possibility of controlling the chemical reactions of radical pair intermediates in different ways.

The design method of adaptive infinite impulse response (IIR) filter is a challenging problem. Its design principle is to determine the filter parameters by the iteration process of the adaptive algorithm, which is to obtain an optimal model for unknown plant based on minimizing mean square error (MSE). However, many adaptive algorithms cannot adjust the parameters of IIR filter to the minimum MSE. Therefore, a more efficient adaptive optimization algorithm is required to adjust the parameters of IIR filter. In this paper, we propose a selfish herd optimization algorithm based on chaotic strategy (CSHO) and apply it to solving IIR system identification problem. In CSHO, we add a chaotic search strategy, which is a better local optimization strategy. Its function is to search for better candidate solutions around the global optimal solution, which makes the local search of the algorithm more precise and finds out potential global optimal solutions. We use solving IIR system identification problem to verify the effectiveness of CSHO. Ten typical IIR filter models with the same order and reduced order are selected for experiments. The experimental results of CSHO compare with those of bat algorithm (BA), cellular particle swarm optimization and differential evolution (CPSO-DE), firefly algorithm (FFA), hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), improved particle swarm optimization (IPSO) and opposition-based harmony search algorithm (OHS), respectively. The experimental results show that CSHO has better optimization accuracy, convergence speed and stability in solving most of the IIR system identification problems. At the same time, it also obtains better optimization parameters and achieves smaller difference between actual output and expected output in test samples.

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

Applying point cloud data to road analysis is crucial for obtaining practical features for segmenting and classifying road point clouds. This study proposes a multi-step method for extracting road points and road network structures from urban Light Detection and Ranging (LiDAR) point cloud data. The first step is a two-step algorithm of coarse grid classification and local optimization of point cloud fine classification. This step extracts road point clouds from various parts of the city. The second step involves the road-point cloud splicing work. Finally, we extract the urban road network structure according to the point cloud of the urban main road and calculate the width of each road. We evaluate the method’s feasibility using four urban road point clouds. Experimental results show that the proposed method can quickly and accurately extract road points, obtaining a road data accuracy and integrity of >94% and a road width estimated relative error of <7%.

In this study, we introduce an innovative approach for the identification of vehicle suspension parameters, employing a methodology that utilizes synthetic and experimental data for non-invasive analysis. Central to our approach is the application of a basic local optimization algorithm, chosen to establish a baseline for parameter identification in increasingly complex vehicle models, ranging from quarter-vehicle to half-vehicle (bicycle) models. This methodology enables the accurate simulation of the vehicle dynamics and the identification of suspension parameters under various conditions, including road perturbations such as speed bumps and curbs, as well as in the presence of noise. A significant aspect of our work is the ability to process real-world data, making it applicable in practical scenarios where data are obtained from onboard sensor equipment. The methodology was developed in MatLab, ensuring portability across platforms that support this software. Furthermore, the study explores the application of this methodology as a tool for denoising, enhancing its utility in real-world data analysis and predictive maintenance. The findings of this research provide valuable insights for vehicle suspension design, offering a cost-effective and efficient solution for dynamic parameter identification without the need for physical disassembly.

Chemical composition analysis, chromatography and spectroscopy are dominate quality evaluation methods for agarwood, which are cumbersome and time-consuming. To facilitate its quality evaluation, a global-to-local optimization method is proposed to automatically inspect the appearance of the burned agarwood stick. First, a dissimilarity coefficient is defined by the attributes of the connected domains to coarsely localize the carbon line region. Then, the threshold for the coarsely localized carbon line region is adaptively determined based on grayscale characteristics of image patches partitioned from the coarsely localized carbon line region. Next, the threshold is used to extract the contour of the carbon line region and to establish the fine localization model for locally and precisely localizing the carbon line region. Finally, an ash shrinkage compensation coefficient is defined to calculate the ash shrinkage rate (ASR). The ASR combined with carbon line height is utilized to characterize the appearance of burned agarwood. Experimental results indicate that the proposed inspection method can well detect the carbon line regions and ashes of burned agarwood sticks, with a mean ASR error of 0.74%, which is superior to some existing inspection methods.

This study introduces the process of improving the accuracy of predicting phase fractions and mechanical properties calculated using numerical analysis in ductile cast iron. To improve the accuracy of the numerical analysis, a method of comparing the measured values of the phase fraction and mechanical properties with the analysis results was used through a step-shaped casting experiment with adjusted thickness. In addition, to compare and verify the numerical analysis results according to chemical composition, experiments were conducted using 5 different alloys. For the error between the value measured through the experiment and the result value calculated by numerical analysis, the accuracy of the analysis was improved by finding the optimal coefficient value in the formula for calculating the phase fraction and mechanical property value using the local optimization method. In addition, to verify the coefficient value obtained through the above method, 5 different products were selected, and the phase fraction and mechanical properties were measured. The measured phase fraction and mechanical property values were compared and analyzed with numerical analysis results to confirm the improved analysis accuracy.

We present a model development framework and numerical solution approach to the general problem-class of packing convex objects into optimized convex containers. Specifically, we discuss the problem of packing ovals (egg-shaped objects, defined here as generalized ellipses) into optimized regular polygons in R2. Our solution strategy is based on the use of embedded Lagrange multipliers, followed by nonlinear optimization. Credible numerical results are attained using randomized starting solutions, refined by a single call to a local optimization solver. We obtain visibly good quality packings for packing 4 to 10 ovals into regular polygons with 3 to 10 sides in all 224 test problems presented here. Our modeling and solution approach can be extended towards handling other difficult packing problems.