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Many-objective evolutionary algorithms often struggle to strike a balance between convergence and diversity when solving many-objective optimization problems. Consequently, an adaptive boundary-based selection many-objective evolutionary algorithm with density estimation, MaOEA/ABS-DE, is proposed. Specifically, the algorithm initially utilizes prior information regarding the adaptive estimation of the shape of the Pareto front to perform coordinate transformation on the population, thus guiding the population towards the approximate boundary of the current Pareto front. Subsequently, a novel approach for estimating the neighboring density of individuals is introduced by utilizing the distribution information of the transformed population. Furthermore, adaptive boundary-based selection and shifted-based density estimation selection strategies are designed for the environmental selection process. The environmental selection process is completed through a two-step selection. In the first step, a candidate pool is formed by selecting solutions with lower neighboring density, which aims to maintain a broad search for the population in the objective space. In the second step, the SDE is introduced to compare the convergence of individuals in the candidate pool, and those with the optimal SDE values are selected. Finally, extensive comparative experiments are conducted on two benchmark test suites, MaF (Many-objective Functions) and WFG (Walking Fish Group), as well as on two practical cases. The MaOEA/ABS-DE is compared with five representative many-objective evolutionary algorithms, including 1by1EA, NSGA-III, MaOEA-CSS, RVEA, and SPEA2 + SDE. Experimental results demonstrate that the proposed algorithm outperforms or at least matches the performance of the five algorithms on 87.04%, 92.59%, 87.04%, 88.89%, and 87.04% of the test instances, respectively. [ABSTRACT FROM AUTHOR]

Purpose: In order to reduce the waste of mineral resources and improve the utilization rate of lean ore, considering the influence of insufficient ore surplus and the limited amount of shovel loading equipment on the selection of loading points in large metal open-pit mines, a loading point selection model was established with the objectives of minimum grade deviation, minimum production cost and shortest total queuing time of vehicles. Method: A two-dimensional matrix integer coding method is adopted, and an indirect quadratic genetic optimization algorithm based on random fragment crossing and specific mutation operators is designed to solve the model. Result: Taking the actual production data of a large metal open-pit mine in China as an example, the simulation results show that the loading point selection scheme can still meet the requirements of the ore allocation plan under the condition that the number of shovel loading equipment is limited and the remaining ore of priority mining is relatively small. Conclusion: The proposed model and algorithm can be generally applied to large-scale metal open-pit mining, and the ore at the loading point can be fully utilized. Significance: It improves the economic benefit of the enterprise and the overall mining efficiency of the mine. [ABSTRACT FROM AUTHOR]

Objective: To establish ulcerative colitis (UC) rat model of spleen deficiency and dampness, and to explore effect of Jianpi-Huazhu pill and its different deconditions on TLR-4/MyD88/NF-κB pathway. Methods: A total of 64 Wistar rats were randomly divided into normal control group (n=8) and model group (n=56). UC rat model was established by traditional Chinese medicine syndrome combined with ethanol-2, 4, 6-trinitrobenzene sulfonic acid enema method. After successfully modeling, rats were randomly divided into model group (M), Mesalazine group (A), Jianpi-Huazhi pill group (B), Jianpi-Qinghua decoction group (C), Jianpi-Qinghua-Huoxue decoction group (D), Jianpi-Qinghua-Daozhi decoction group (E) and Qinghua-Daozhi-Huoxue decoction group (F), with 8 rats in each group, given corresponding drugs by gavage for 4 weeks. At end of course of treatment, all rats were sacrificed and samples were taken. Changes of body weight, disease activity and pathological changes of colon were observed. TNF-α expression in colon tissue was detected by ELISA and immunohistochemistry. RT-PCR and Western blot were used to detect mRNA and protein expressions of TLR-4, MyD88 and NF-κB in rat colon tissue. Results: Rats weight in Jianpi-Huazhi pill group, Jianpi- Qinghua decoction group, Jianpi-Qinghua-Huoxue decoction group and Jianpi-Qinghua-Daozhi decoction group were obviously higher than that of model group (P<0.01), all treatment groups could obviously improve UC model rat pathological damage and disease activity index in colon tissue, and Jianpi-Huazhi pill group and Jianpi-Qinghua-Huoxue group showed better efficacy. All treatment groups could inhibit mRNA and protein expressions of TLR-4, MyD88 and NF-κB in colon tissue (P<0.001). Mesalazine group, Jianpi-Huazhi pill group, Jianpi-Qinghua-Daozhi group and Qinghua-Daozhi-Huoxue group were significantly better than Jianpi-Qinghua group and Jianpi-Qinghua-Huoxue group in reducing NF-κB mRNA and protein expressions (P<0.05). All groups could reduce expression of TNF-α in colon tissue (P<0.001), and there was no significant difference between groups (P>0.05). Conclusion: Jianpi- Huazhi pill and its various deconditions may play roles through TLR-4/MyD88/NF-κB pathway. Among them, Coptid-Rhizoma coptifolia, stews woody incense, Fengwei herb and gung ginger may be core drugs of this recipe, which has certain guiding significance for clinical practice. [ABSTRACT FROM AUTHOR]

For expensive problems, as the number of objectives and the number of decision variables increase, the number of non-dominated solutions in the population increases dramatically, and the population does not have enough convergence pressure to converge to the true pareto frontier. A Kriging-assisted Multi-stage Evolutionary Algorithm (K-MSEA) is proposed to enhance the exploratory capacity of populations in expensive many-objective optimization problems. A multi-stage strategy is used in K-MSEA. Mating selection and environmental selection of populations are divided into three stages based on convergence and diversity indices. Targeted selection of individuals should be conducted at each stage of population development. The population independently chooses the appropriate stage in accordance with the current demands of convergence and diversity. Differential evolutionary algorithm is used to improve the search ability of the population. To guide the population in the correct direction, K-MSEA employs an elite retention strategy in environmental selection. The population utilizes individual renewal, where underperforming parents are replaced by better-performing offspring. The surrogate model is then updated with the uncertainty information supplied by the Kriging model. Finally, K-MSEA is benchmarked against five other algorithms in experiments on two benchmark problems and two realistic expensive problems. Mathematical analysis demonstrates that K-MSEA is more competitive than others. [ABSTRACT FROM AUTHOR]

In practical applications, the optimal solutions of multi-objective optimization are not unique. Some problems exist different Pareto Sets (PSs) in the decision space mapped to the same Pareto Front (PF) in the objective space, which are called multimodal multi-objective problems (MMOPs). To tackle this issue, this paper proposes a multimodal multi-objective optimization based on a local optimal neighborhood crowding distance differential evolution algorithm. First, an adaptive partitioning strategy in the initialization phase is proposed by using the characteristics of the heuristic stochastic search. That ensures the local optimal solution is quickly found among multiple PSs. Second, opposition-based learning is combined with differential mutation to generate vectors, which accelerate the convergence of the population to the optimal solution. Finally, a method for neighborhood crowding distances on different Pareto ranks is designed. The distance is computed by a weighted sum of Euclidean distances for the nearest neighbors. While reducing computational complexity, this strategy reflects realistic crowding degree. With these methods, balances the diversity performance of the decision and the objective space, while improving the search capability. Multiple PSs reveal the problem's potential characteristics and meet the needs of the decision-maker. The practical significance is verified by the application of actual distance minimization problem. According to experimental results, the proposed method can achieve a high level of comprehensive performance. [ABSTRACT FROM AUTHOR]

For solving complex many-objective optimization problems, the many-objective evolutionary algorithms may require new distance measures and more enormous selection pressures to guide solutions to the Pareto front. In this case, solving irregular Pareto front problems with growing neural gas networks as reference vectors is a great challenge. This paper proposes an algorithm with improved growing neural gas and angle-penalized distance to overcome the difficulty: First, a new parameter, the proportion of the solutions relatively close to the input data, is introduced to the angle-penalty distance, boosting the influence on the entire environment selection and enhancing the effect for Pareto front learning. Second, the edge-acceleration strategy enhances the ability of growing neural gas to match Pareto fronts and adapt to rapidly changing input. Finally, by adjusting the nodes deletion strategy in the search procedure, more promising solutions may be maintained to optimize the solutions' distribution. In experiments tackling problems with irregular Pareto fronts, the proposed algorithm performs competitively in contrast to the other six excellent algorithms. [ABSTRACT FROM AUTHOR]

There are many large-scale sparse multi-objective optimization problems in real life, but it is difficult to solve such problems due to the high dimension of decision variables and the excessive sparseness of Pareto optimal solutions. To address these issues, this paper proposes a quadratic association vector and dynamic guided operator search algorithm for large-scale sparse multi-objective optimization problems. Firstly, according to the individual distribution in the decision space, the reference vector is used to quadratically associate the individual, aiming to find better solutions during the initialization process. Secondly, the dynamic guided operator is introduced into the crossover mutation. The population is guided to evolve toward the sparse optimal solution by flipping the real and binary variables according to the dynamic guided operator. Thirdly, individuals are selected into the mating pool for crossover mutation based on the decision variables of the population individuals, with the aim of further enhancing the convergence of the algorithm. The experimental results on eight benchmark problems show that the algorithm obtains the best comprehensive performance on 66.7% of the test problems. The proposed algorithm is superior to the existing algorithm in terms of effectiveness. [ABSTRACT FROM AUTHOR]

Most genetic operators use random mating selection strategy and fixed rate crossover operator to solve various optimization problems. In order to improve the convergence and diversity of the algorithm, an adaptive adjacent maximum distance crossover operator is proposed in this paper. A new mating selection strategy (distance-based mating selection strategy) and an adaptive mechanism (adaptive crossover strategy based on population convergence) are adopted. Distance-based mating selection strategy purposefully selects parents to produce better offspring. Adaptive crossover strategy based on population convergence increases the convergence speed of the algorithm by controlling the crossover probability. The proposed crossover strategy is evaluated on the simulated binary crossover operators of non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. The performance of the algorithm is verified on a series of standard test problems. Finally, the optimization results of the improved algorithm using adaptive adjacent maximum distance crossover operator and the standard algorithm are compared and analyzed. The experimental results show that the algorithm using adaptive adjacent maximum distance crossover operator has better optimization results. [ABSTRACT FROM AUTHOR]

Recent research shows that the Rotation Forest and its several variants can achieve better performance than other widely used ensemble methods in classification issues. However, it is a very time-consuming task to train a Rotation Forest on large-scale and high-dimensional data. To improve its classification accuracy and reduce the computational cost, a novel classification ensemble algorithm named RoF-GBM is presented in this paper. In the novel algorithm, singular value decomposition is employed to solve the rotation matrix, and then whitening is performed to reduce the computational complexity of PCA. And LightGBM is utilized to train the base classifier to reduce the number of PCA calculations of the whole model. The effectiveness of the proposed RoF-GBM is evaluated against twelve small and medium-scale datasets, three large-scale datasets, three high-dimensional datasets, two artificial datasets and five comparison algorithms. And the extensive experimentation demonstrates that RoF-GBM can observably improve the training speed of Rotation Forest and generate a higher classification accuracy than LightGBM and the variants of Rotation Forest, such as RotBoost and Rotation of Random Forest. Moreover, RoF-GBM also shows strong robustness on some datasets with noise and extreme outliers. [ABSTRACT FROM AUTHOR]

Striking a balance between convergence and diversity matters considerably for evolutionary algorithms in solving many-objective optimization problems. The performance of these algorithms depends on their capability of obtaining a set of uniformly distributed solutions as close to the Pareto optimal front as possible. However, most existing evolutionary algorithms encounter challenges in solving many-objective optimization problems. Thus, in this paper, an adaptive many-objective evolutionary algorithm with coordinated selection strategies, labeled ACS-MOEA, is proposed to balance the convergence and diversity. The coordinated selection strategies include three selection strategies, i.e., the selection based on shifted-dominated distance, the selection based on objective vector angle, and the selection based on Non-Euclidean geometry distance. The first is used in the mating selection process to select high-quality parents for the generation of good offspring. Both the second and the third selection strategies are employed in the environmental selection process to delete poor solutions one by one for preserving the elitist solutions of the next generation. The performance of ACS-MOEA is verified by comparing it with six state-of-the-art algorithms on several well-known benchmark test suites with up to 10 objectives. Experimental results have fully demonstrated the competitiveness of ACS-MOEA in balancing convergence and diversity. Moreover, the proposed ACS-MOEA has also been verified to be effective in solving constrained many-objective optimization problems. [ABSTRACT FROM AUTHOR]

In order to solve the problems of a slow solving speed and easily falling into the local optimization of an ore-blending process model (of polymetallic multiobjective open-pit mines), an efficient ore-blending scheduling optimization method based on multiagent deep reinforcement learning is proposed. Firstly, according to the actual production situation of the mine, the optimal control model for ore blending was established with the goal of minimizing deviations in ore grade and lithology. Secondly, the open-pit ore-matching problem was transformed into a partially observable Markov decision process, and the ore supply strategy was continuously optimized according to the feedback of the environmental indicators to obtain the optimal decision-making sequence. Thirdly, a multiagent deep reinforcement learning algorithm was introduced, which was trained continuously and modeled the environment to obtain the optimal strategy. Finally, taking a large open-pit metal mine as an example, the trained multiagent depth reinforcement learning algorithm model was verified via experiments, with the optimal training model displayed on the graphical interface. The experimental results show that the ore-blending optimization model constructed is more in line with the actual production requirements of a mine. When compared with the traditional multiobjective optimization algorithm, the efficiency and accuracy of the solution have been greatly improved, and the calculation results can be obtained in real-time. [ABSTRACT FROM AUTHOR]

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds. [ABSTRACT FROM AUTHOR]

Background: Dermatofibrosarcoma protuberans (DFSP) is a kind of low‐grade malignant spindle cell neoplasm, the diagnosis, and treatment, which have markedly attracted clinicians' attention for its repeated recurrence. High‐resolution magnetic resonance imaging (HR‐MRI) has shown unique capabilities in diagnosis of various cutaneous tumors. Materials and methods: Data of 29 patients with clinically suspected DFSPs and undergoing dynamic contrast‐enhanced (DCE) HR‐MRI preoperatively were prospectively collected. The HR‐MRI qualitative features were evaluated and compared. The DCE‐associated quantitative parameters and the time‐signal intensity curve (TIC) types were provided using DCE sequences. Results: A total of 7 DFSPs, nine dermatofibromas (DF, including four cases of cellular variant [CDF]), 12 keloids, and one nodular fasciitis were enrolled. DFSP showed the largest major diameter and the deepest depth. Five DFSPs (71.4%) showed ill‐defined margins as well as infiltration of peripheral adipose. All DFSPs showed irregular shape. Most DFSPs presented hyperintensity on T2WI (71.4%) and iso‐intensity on T1WI (85.7%). Six cases (85.7%) had significant enhancement, and six cases (85.7%) had homogeneous enhancement. There were significant differences of Ktrans, Kep, Ve and iAUC values among DFSPs, DFs, and keloids, and DFSP had the highest values for these parameters. Six DFSPs (85.7%) and four CDFs (100%) showed type‐III TICs, while the other lesions showed type‐Ⅰor type‐Ⅱ TICs. Conclusions: DCE‐HR‐MRI could show the growth characteristics of DFSPs, which was of great value for the diagnosis and differential diagnosis of DFSPs and was helpful for the determination of treatment options, thereby to improve the prognosis of patients. [ABSTRACT FROM AUTHOR]

A serious problem faced by the metal mineral mining industry is the challenge to the sustainable development of resource mining due to the continuous decline of ore geological grade. In the case of producing concentrates of the same quality, compared with using only high-grade raw ore, ore blending is a way to slow down the decline of ore geological grade by combining high- and low-grade raw ore. There are many ore blending models considering cost minimization or profit maximization as the target value, ignoring the fact that ore blending is intended to obtain a homogenized product. Moreover, the ore blending model cannot be solved by traditional operational research methods when blended grade stability of multiple elements is considered in the ore blending program. In this paper, a multi-objective ore blending optimization model is constructed for the comprehensive utilization of associated resources in ores. It minimizes the deviation of the grade of each metallic element in the blended associated ore from the beneficiation grade and the percentage of different types of rocks at the unloading point. To solve this multi-objective optimization model, an intelligent optimization method is proposed that is an improved multi-objective optimization algorithm based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III). The case study shows that the proposed model and algorithm can effectively solve the mixing problem of polymetallic ores and obtain a satisfactory ore blending solution. [ABSTRACT FROM AUTHOR]

Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems. [ABSTRACT FROM AUTHOR]

5G communication network is vital to the intelligent and unmanned technology of open-pit mine, but the high cost of 5G base station deployment will bring huge cost increases to mining enterprises. Thus, this article proposes an improved sparrow search algorithm (SSA) using random walk strategy (RWSSA) to optimization the distribution and signal coverage of 5G base stations in open-pit mine. In order to verify the performance of the algorithm, the SSA, the modified ant lion optimizer, the particle swarm optimization, and the RWSSA were used to compare and analyze the accuracy and speed of the algorithm in solving high dimensionality benchmark functions, respectively. The dimensions used in the test are 30, 100, and 500 dimensions. After that, four algorithms were applied to a real example to optimize the deployment of 5G base stations, including macrobase stations and microbase stations. RWSSA has very good performance in terms of performance and practical application. Therefore, RWSSA is more suitable for the application of 5G base station distribution optimization in open-pit mines. [ABSTRACT FROM AUTHOR]

Capillarity influences the long-term stability of the dam greatly and affects the sustainable development of tailings dam. The online monitoring capillary water rise test system was independently developed by SM3001B temperature and humidity sensor, SU9101B RS485 converter, SV3010 data acquisition system, and other equipment to carry out the whole process test of water absorption and water release of tailings with different particle sizes. The results show that the capillary rising trend of tailings with different particle sizes is basically consistent, the relationship between the rising height and the diameter are negatively correlated, and the logarithm function can better fit the height of capillary rising over time well enough. The water content of tailing capillary zone decreases with the height, and the smaller the size of tailings, the greater the change of water content. The water release process of tailings is an unsaturated permeability process, which largely depends on the saturation before water release. The larger the particle size of tailings is, the smaller the water content after water release. Based on the above results, the relationship between capillary diameter and tailings particle size was discussed, and the relationship formula between water content change and particle size was deduced. The research results systematically show the water migration law under the tailing capillaries. It is suggested that the unsaturated tailings water content index should be added in the monitoring of the saturated surface of tailings dam. [ABSTRACT FROM AUTHOR]

When the surrogate-assisted evolutionary algorithm is used to solve expensive many-objective optimization problems, the surrogate is used to approximate the expensive fitness functions. However, with the increase of the number of objectives, the approximate error of the surrogate will accumulate gradually, and the computational cost will also increase sharply. This paper proposes an improved bagging ensemble surrogate-assisted evolutionary algorithm (IBE-CSEA) to solve these problems. An ensemble classifier is used to classify the offspring instead of building the surrogate to approximate the fitness function of each objective. Firstly, a group of classification boundary individuals are selected one by one from the individuals evaluated by the expensive fitness function. All the individuals evaluated by the expensive fitness function are divided into two categories; Secondly, the individuals in these two categories are divided into the training set and the test set. The training set is used to train an improved bagging ensemble classifier. The test set is used to calculate the reliability of the classification; Finally, the classification results and the reliability are used to select the promising individuals for expensive fitness function evaluation. Compared with the current popular surrogate-assisted evolutionary algorithm, IBE-CSEA algorithm is more competitive. [ABSTRACT FROM AUTHOR]

Multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world. [ABSTRACT FROM AUTHOR]

In the production process of open-pit mines, transportation costs account for 45–60% of the total cost. Therefore, a reasonable and economic transportation system can effectively improve production efficiency. In this article, optimization of the layout of the fixed crushing station in the in-pit crusher and conveyor system of open-pit mining is studied. First, the entire ore body is discretized into multiple ore blocks. By calculating the transportation distance between each block and the crushing station, an optimized model for minimizing the total transportation work is established. Finally, combining the advantages of particle swarm optimization (PSO) and quantum PSO (QPSO), a two-stage fusion particle swarm optimization algorithm (TSF-PSO) is designed to solve the model, and the effectiveness of the model is verified by actual cases. Experimental data show that, compared with the PSO and QPSO algorithms, the solution efficiency of the TSF-PSO algorithm is improved by 2.83% and 1.48%, respectively. [ABSTRACT FROM AUTHOR]

Road extraction in open-pit mine is one of the important tasks of intelligent dispatching and manless driving. However, traditional methods for extracting open-pit roads cannot capture detailed information about roads, such as shape and surrounding environment. An MD-LinkNeSt (Multi Dilation-LinkNeSt) network is proposed to extract the open-pit road. First, the classification of UAV remote sensing image in mining area was completed based on mine road features. Second, ResNeSt and LinkNet are used for the encoding and decoding of MD-LinkNeSt network. The MD module includes encoding and decoding to form MD-LinkNeSt network.MD-LinkNeSt, a network framework for road extraction from open-pit mines, is constructed. Third, to enhance the extraction effect of MD-LinkNeSt network on open-pit road, the road loss function is added to MD-LinkNeSt network. Compared with the networks of Unet, DUnet, LinkNet and D-LinkNet, the network in this paper has higher IOU (Intersection over Union) and better continuity. Finally, the road network model is generated by using the open-pit mine road extraction method. The results show that this method can not only extract the road details better than the traditional method, but also meet the intelligent extraction accuracy requirements of the main road network in the open-pit mine. [ABSTRACT FROM AUTHOR]

In the field of evolutionary algorithms, Pareto‐based algorithms are less effective when more than three objectives are encountered, due to the lack of sufficient selection pressure. In this paper, a Pareto‐based many‐objective evolutionary algorithm with reference direction and coordinated selection strategy, abbreviated as SPEACSS, is proposed. The algorithm inherits the fitness calculation strategy of the strength Pareto evolutionary algorithm, while it applied an efficient reference direction‐based density estimator and a novel selection strategy. Moreover, mating selection and environmental selection are complementary and coordinated in the evolutionary process and have better performance than be used alone. In the criteria of mating selection, a method is given to improve the effectiveness of the parent combination. Experimental results on benchmark functions show that the proposed algorithm is superior to several state‐of‐the‐art designs, and demonstrate the effectiveness of the improved algorithm in balancing diversity and convergence. [ABSTRACT FROM AUTHOR]

Dominance resistance is a challenge for Pareto-based multi-objective evolutionary algorithms to solve the high-dimensional optimization problems. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) still has such disadvantage even though it is recognized as an algorithm with good performance for many-objective problems. Thus, a variation of NSGA-III algorithm based on fine final level selection is proposed to improve convergence. The fine final level selection is designed in this way. The θ-dominance relation is used to sort the solutions in the critical layer firstly. Then ISDE index and favor convergence are employed to evaluate convergence of individuals for different situations. And some better solutions are selected finally. The effectiveness of our proposed algorithm is validated by comparing with nine state-of-the-art algorithms on the Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group test suits. And the optimization objectives are varying from 3 to 15. The performance is evaluated by the inverted generational distance (IGD), hypervolume (HV), generational distance (GD). The simulation results show that the proposed algorithm has an average improvement of 55.4%, 60.0%, 63.1% of 65 test instances for IGD, HV, GD indexes over the original NSGA-III algorithm. Besides, the proposed algorithm obtains the best performance by comparing 9 state-of-art algorithms in HV, GD indexes and ranks third for IGD indicator. Therefore, the proposed algorithm can achieve the advantages over the benchmarks. [ABSTRACT FROM AUTHOR]

In order to solve the ill-posed problem in the process of reconstructing the spectral reflectance of the traditional matrix R method, a regularization matrix R method was proposed in this paper. Through analyzing the ill-posed equation of matrix R to reconstruct the spectral reflectance, the Tikhonov regularization method was researched to restrict the ill-posed problem to solve the Moore-Penrose pseudo inverse matrix. The L-curve method was used to obtain the optimal regularization parameter by training samples data in order to effectively restrict the ill-posed situation which was caused by the equation solving of spectral reconstruction. The experimental results verified that the proposed regularization matrix R method had higher spectral and chromatic accuracy of reconstructed spectrum than traditional matrix R method. At the same time, the proposed regularization matrix R method achieved good performance for color reproduction of real mural in practical application. [ABSTRACT FROM AUTHOR]

With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform. [ABSTRACT FROM AUTHOR]