Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Optimization of Open-cast Mining Procedure Based on RSR Method

To explore the optimal evaluation mechanism of open-cast mining procedure,this paper takes the actual operation status of Huolinhe No.1 Open-cast Mine as the research basis,and makes a deep analysis of the four representative mining procedures proposed by this mine.A detailed and comprehensive evaluation system is constructed using rank-sum ratio(RSR)method.The system covers 17 key indicators and aims to evaluate the advantages and disadvantages of each scheme in an all-round and multi-angle manner.Through the calculation and analysis by RSR method,the comprehensive evaluation of the four types of mining procedure schemes is carried out,and finally the secondary river improvement project is determined as the optimal mining implementation scheme,and the joint mining scheme of the south and north areas is the alternative strategy.The research results of this paper are objective,clear and definite,can not only reveal the effectiveness and feasibility of RSR method in solving the problem of open-cast mining procedure optimization,but also provide a strong technical support and decision-making basis for the future production development of Huolinhe No.1 Open-cast Mine.Thus,this study is expected to further promote the scientific and refined process of mining operations.

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing rapidly.The coal mines have to pace up the mining operations with large production to meet the energy demand.This requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a few.Therefore,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(i.e.multi-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS models.Accordingly,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile strength.The MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines.The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

Optimization model of unascertained measurement for underground mining method selection and its application

An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.

Optimization principle of combined surface and underground mining and its applications

The pit limit optimization is discussed, which is one of the most important problems in the combined min-ing method, on the basis of the economic model of ore-blocks. A new principle of the limit optimization is put for-ward through analyzing the limitations of moving cone method under such conditions. With a view to recovering asmuch mineral resource as possible and making the maximum profit from the whole deposit, the new principle is tomaximize the sum of gain from both open-pit and underground mining. The mathematical models along the horizon-tal and vertical directions and modules for software package (DM&MCAD) have been developed and tested inTonglushan Copper Mine. It has been proved to be rather effective in the mining practice.

Optimization on cut-hole of mining tunnel excavation

The efficiency of excavation a mining tunnel is definitely linked with modes of cut-holes. According to experience and methods of engineering analogy, the double-wedge cut, the 9-hole cut and the single spiral cut were determined originally by considering the production conditions and blasting environment of the mining tunnels of the-74 m horizontal in the Da-ye iron mine. Based on acquired modes of cut-holes, the effect of the cut was studied, on the one hand, by a numerical simulation method with the aid of LS-DYNA3D, a nonlinear dynamic finite element program; on the other hand, a spot experiment was carried out in the mining tunnels. Both the numerical simulation and the spot experiment demonstrated and agreed that a single spiral cut provides the optimum excavation effect.

Highwall mining of thick,steeply dipping coal–A case study in geotechnical design and recovery optimization

Highwall mining of thick(up to 30.48 m)steeply dipping(20°or more)coal seams provides many chal lenges,both geotechnically and operationally,as seam dips near or in excess of highwall mining machine capabilities are encountered.Maximizing coal recovery while maintaining highwall stability requires innovative techniques with regard to web and barrier pillar layout,depth of penetration,and choice of mining horizon within the seam.Stability of highwall mining slopes,openings,and pillarsare typically analyzed using the ARMPS-HWM program,as well as LAMODEL,UDEC and SLOPE/W modeling.Highwall stability can be maintained,and highwall mining production optimized by applying design cri-teria in creative ways,including alternating miner penetration depths and initiating mining of thick seams toward the bottom of the seam.Highwall mining of thick,steeply dipping coal requires careful planning and execution,including close cooperation between geotechnical design engineers,the mining company,and the highwall mining contractor.This paper describes the application of creative design techniques to a specific pit arrangement at the Westmoreland Kemmerer Mine,Kemmerer,Wyoming.Highwall mining was accomplished by UGM ADDCAR Systems,LLC on a contract basis.

Dynamic optimization of cutoff grade in underground metal mining

In order to maximize the overall economic gain from a metal mine operation, selection of cutoff grades must consider two important aspects: the time value of money and the spatial variation of the grade distribution in the deposit. That is, cutoff grade selection must be dynamic with respect to both time and space. A newly developed method that fulfills these requirements is presented. In this method, the deposit or a portion of it under study is divided into "decision units" based on the mining method and sample data. The statistical grade distribution and the grade-tonnage relationship of each decision unit are then computed based on the samples falling in the unit. Each decision unit with its grade-tonnage relationship is considered as a stage in a dynamic programming scheme and the problem is solved by applying a forward dynamic programming based algorithm with an objective function of maximizing the overall net present value (NPV). A software package is developed for the method and applied to an underground copper mine in Africa.

Optimization of clay material mixture ratio and filling process in gypsum mine goaf

Because there is neither waste rock nor mill tailings in the gypsum mine, and the buildings on the goaf of gypsum mine are needed to be protected, the research proposed the scheme of the clay filling technology. Gypsum, cement, lime and water glass were used as adhesive, and the strength of different material ratios were investigated in this study. The influence factors of clay strength were obtained in the order of cement, gypsum, water glass and lime. The results show that the cement content is the determinant influence factor, and gypsum has positive effects, while the water glass can enhance both clay strength and the fluidity of the filing slurry. Furthermore, combining chaotic optimization method with neural network, the optimal ratio of composite cementing agent was obtained. The results show that the optimal ratio of water glass, cement, lime and clay (in quality) is 1.17:6.74:4.17:87.92 in the process of bottom self-flow filling, while the optimal ratio is 1.78:9.58:4.71:83.93 for roof-contacted filling. A novel filling process to fill in gypsum mine goaf with clay is established. The engineering practice shows that the filling cost is low, thus, notable economic benefit is achieved.

Multidisciplinary design optimization on production scale of underground metal mine

In order to ensure overall optimization of the underground metal mine production scale, multidisciplinary design optimization model of production scale which covers the subsystem objective function of income of production, safety and environmental impact in the underground metal mine was established by using multidisciplinary design optimization method. The coupling effects from various disciplines were fully considered, and adaptive mutative scale chaos immunization optimization algorithm was adopted to solve multidisciplinary design optimization model of underground metal mine production scale. Practical results show that multidisciplinary design optimization on production scale of an underground lead and zinc mine reflect the actual operating conditions more realistically, the production scale is about 1.25 Mt/a (Lead and zinc metal content of 160 000 t/a), the economic life is approximately 14 a, corresponding coefficient of production profits can be increased to 15.13%, safety factor can be increased to 5.4% and environmental impact coefficient can be reduced by 9.52%.

Frequent item sets mining from high-dimensional dataset based on a novel binary particle swarm optimization

A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.

Strategic mining options optimization:Open pit mining, underground mining or both

Near-surface deposits that extend to considerable depths are often amenable to both open pit mining and/or underground mining. This paper investigates the strategy of mining options for an orebody using a Mixed Integer Linear Programming(MILP) optimization framework. The MILP formulation maximizes the Net Present Value(NPV) of the reserve when extracted with(i) open pit mining,(ii) underground mining, and(iii) concurrent open pit and underground mining. Comparatively, implementing open pit mining generates a higher NPV than underground mining. However considering the investment required for these mining options, underground mining generates a better return on investment than open pit mining. Also, in the concurrent open pit and underground mining scenario, the optimizer prefers extracting blocks using open pit mining. Although the underground mine could access ore sooner, the mining cost differential for open pit mining is more than compensated for by the discounting benefits associated with earlier underground mining.

A value adding approach to hard-rock underground mining operations: Balancing orebody orientation and mining direction through meta-heuristic optimization

Underground mines require complex construction activities including the shaft, levels, raises, winzes and ore passes. In an underground mine based on stoping method, orebody part(s) maximizing profit should be determined. This process is called stope layout optimization (SLO) and implemented under site-specific geotechnical, operational and economic constraints. For practical purpose, the design obtained by SLO shows consecutive stopes in one path, which assists in defining the mining direction of these stopes. However, this direction may not accommodate the spatial distribution of the ore grade: if the orebody orientation and mining direction differ, the value of the mining operation may decrease. This paper proposes an approach whereby paths in the SLO are defined as decision variables to avoid the cost of mining in the wrong direction. Furthermore, in the genetic-based formulation, which accounts for orebody uncertainty, a robust cluster average design process is proposed to improve SLO’s performance regarding metal content. A case study in narrow gold vein deposit shows that the profit of an underground mining operation could be underestimated by 25%-48% if the algorithm ignores stope layout orientation.

Failure mechanism of pump chambers and their optimized design in deep mining at Qishan Coal Mine

Pump chambers, normally used as dominant structures in mining engineering to insure the safety and production of un-derground coal mines, become generally deformed under conditions of deep mining. Given the geology and engineering condition of Qishan Coal Mine in Xuzhou, the failure characteristics of pump chambers at the –1000 m level show that the main cause can be attributed to the spatial effect induced by intersectional chambers, where one pump is constructed per well. We developed an opti-mized design of the pump room, in which the pump wells in the traditional design are integrated into one compounding well. We suggest that the new design can limit the spatial effect of intersectional chambers during construction given our relevant numerical simulation. The new design is able to simplify the structure of the pump chamber and reduce the amount of excavation required. Based on a bolt-mesh-anchor with a rigid gap coupling supporting technology, the stability of pump chamber can be improved greatly.

Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

Technological optimization of fully mechanized caving mining face with large mining heights

Fully mechanized cave mining with large mining height is a new mining method, due to its large mining thickness and lower roadway excavation, the technology has been widely used in China＇s thick seam mining. In order to improve the top-coal recovery ratio of fully mechanized cave mining with large mining height, a study was conducted on optimizing the caving process, based on the mechanized caving face 1302N in Longgu Coal Mine. This was achieved by improving the PFC numerical calculation methods, and establishing a more accurate model system. On this basis, the recovery ratio of the top coal in different drawing intervals and technologies was investigated in order to achieve a reasonable caving process. The top-coal tracking system was used for practical surveying of the recovery ratio of top coal.

Identifcation of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

An approach which combines particle swarm optimization and support vector machine(PSO–SVM)is proposed to forecast large-scale goaf instability(LSGI).Firstly,influencing factors of goaf safety are analyzed,and following parameters were selected as evaluation indexes in the LSGI:uniaxial compressive strength(UCS)of rock,elastic modulus(E)of rock,rock quality designation(RQD),area ration of pillar(Sp),the ratio of width to height of the pillar(w/h),depth of ore body(H),volume of goaf(V),dip of ore body(a)and area of goaf(Sg).Then LSGI forecasting model by PSO-SVM was established according to the influencing factors.The performance of hybrid model(PSO+SVM=PSO–SVM)has been compared with the grid search method of support vector machine(GSM–SVM)model.The actual data of 40 goafs are applied to research the forecasting ability of the proposed method,and two cases of underground mine are also validated by the proposed model.The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search,and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust,which might hold a high potential to become a useful tool in goaf risky prediction research.

Simulation and optimization approach for uncertainty-based short-term planning in open pit mines

Accuracy in predictions leads to better planning with a minimum of opportunity lost. In open pit mining,the complexity of operations, coupled with a highly uncertain and dynamic production environment,limit the accuracy of predictions and force a reactive planning approach to mitigate deviations from original plans. A simulation optimization framework/tool is presented in this paper to account for uncertainties in mining operations for robust short-term production planning and proactive decision making. This framework/tool uses a discrete event simulation model of mine operations, which interacts with a goalprogramming based mine operational optimization tool to develop an uncertainty based short-term schedule. Using scenario analysis, this framework allows the planner to make proactive decisions to achieve the mine's operational and long-term objectives. This paper details the development of simulation and optimization models and presents the implementation of the framework on an iron ore mine case study for verification through scenario analysis.

URL Phishing Detection Using Particle Swarm Optimization and Data Mining

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

Optimization of industrial structure： crux of the transition of mining cities

The rise and development of mining cities in our country take an extremely special status and play an important role in the national economy and social development. However, there are many problems in mining cities and their industrial structure. To realize the transition of mining cities, we must optimize the industrial structure. According to the theory of the optimization of industrial structure, the strategy is to organize regional enterprise groups, develop cluster economy, continuing industry and circular economy, and promote the rationalization of cities and regional industrial structure.