An approach to evaluation of environmental benefits for ecological mining areas based on ant Colony algorithm

Wan, Linwei; Du, Chunlei

doi:10.1007/s12145-021-00582-w

Cited by 8 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the basis of using the grid method is to construct a map model of the virtual simulation space, and the influence of other interference factors is not considered in the experiment. Three different path planning algorithm models are designed, including Genetic Algorithm (GA) [ 25 ], Ant Algorithm (ACO) [ 26 ], and Deep Q Network Algorithm (DQN). In the same virtual simulation environment, by changing the number of distribution units, the amount of distribution tasks completed in unit time R is calculated, and the value of R is used to compare the performance of the algorithm.…”

Section: Methodsmentioning

confidence: 99%

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Zhao

Wang

2022

Neural Comput & Applic

View full text Add to dashboard Cite

In today's severe situation of the global new crown virus raging, there are still efficiency problems in emergency resource scheduling, and there are still deficiencies in rescue standards. For the happiness and well-being of people's lives, adhering to the principle of a community with a shared future for mankind, the emergency resource scheduling system for urban public health emergencies needs to be improved and perfected. This paper mainly studies the optimization model of urban emergency resource scheduling, which uses the deep reinforcement learning algorithm to build the emergency resource distribution system framework, and uses the Deep Q Network path planning algorithm to optimize the system, to achieve the purpose of optimizing and upgrading the efficient scheduling of emergency resources in the city. Finally, through simulation experiments, it is concluded that the deep learning algorithm studied is helpful to the emergency resource scheduling optimization system. However, with the gradual development of deep learning, some of its disadvantages are becoming increasingly obvious. An obvious flaw is that building a deep learning-based model generally requires a lot of CPU computing resources, making the cost too high.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Zhao

Wang

2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Through these calculations, the convergence speed of the algorithm can be quickly improved. However, due to the characteristics of the ant colony algorithm itself, the search speed is still slow, so it needs to be further improved to avoid premature [ 20 ]. After each cycle, only one ant pheromone is set to update to find the optimal solution for the current cycle.…”

Section: Methodsmentioning

confidence: 99%

Research on Urban National Sports Fitness Demand Prediction Method Based on Ant Colony Algorithm

Yue

Dai

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the development of competitive sports, the enthusiasm of the public to participate in sports has gradually increased. At present, almost all streets in the city have their own fitness places, which provide a lot of help for public fitness. However, the existing fitness venues are obviously insufficient, the venues are limited, relatively single, and the open-space area is insufficient, which cannot meet the needs of mass sports fitness. Based on this, this paper studies and analyzes the prediction of urban national sports fitness demand based on the ant colony algorithm. First, this paper analyzes the National Fitness Situation and the related research on demand forecasting and puts forward the use of the ant colony algorithm to realize demand forecasting. This paper expounds on the research methods and algorithms commonly used in demand forecasting. The ant colony algorithm is used to improve the fuzzy analysis. The urban national sports fitness demand is divided into six secondary indicators, and different tertiary indicators are divided under each secondary indicator. Through simulation analysis, it is confirmed that the improved algorithm proposed in this paper converges faster and finds the best path most. At the same time, the weight of the urban national sports fitness demand index is calculated.

show abstract

“…Lin et al [12] proposed a design method combining AHP and TOPSIS to assist designers in identifying customer needs and design characteristics more efficiently, thus achieving an effective evaluation of design schemes. Wan et al [13] used an ant colony algorithm to optimize the environmental benefits of ecological mining areas based on classification index formulas and comprehensive index formulas, and established an environmental benefit evaluation model. To generate the optimal process planning, Wang et al [14] proposed a fault feature optimization method for remanufacturing process planning, which combined a genetic algorithm (GA) and an artificial neural network (ANN) to optimize the remanufacturing process plan.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Code name Description of FR FR 11 Rotating work piece function FR 12 Coordinated transmission component function FR 13 Servo drive function FR 14 Guide function FR 15 Tool drive function FR 16 Supporting and providing track function (25) According to the correlation matrices B 1 2 and B 2 2 , the noncompatible node connections were obtained. The model figure M 21 of the scheme generation process was established as shown in Figure 12.…”

Section: Source Of Function Requirements (Fr S ) Dp 1 Mechanical Devicementioning

confidence: 99%

Product Design Scheme Generation and Optimization Decisions While Considering Remanufacturability

et al. 2022

View full text Add to dashboard Cite

Social awareness of the environment has promoted the vigorous development of remanufacturing. Traditional product design does not consider the remanufacturability, which leads to improper disposal at the end of the product’s life, resulting in environmental pollution and resource waste. In this paper, a method for the generation and optimization of product design schemes was established, in which remanufacturability was included at the early design stage of the product. Firstly, based on axiomatic design, the Z-shaped mapping was upgraded to the tree topology mapping, which was then incorporated into the scheme generation model, and seven remanufacturability design constraint criteria were used as constraints to obtain a product design set of scenarios. Secondly, the entropy weight method and analytic hierarchy process were combined to calculate the weights of the four evaluation indicators: functionality, economy, stability, and environment; and a differential evolution algorithm was used to optimize the scheme. Finally, a lathe was taken as a case to illustrate the applicability and effectiveness of the proposed methodology. The results showed that the method could successfully generate product design schemes that improved remanufacturability and met the needs of users.

show abstract

An approach to evaluation of environmental benefits for ecological mining areas based on ant Colony algorithm

Cited by 8 publications

References 14 publications

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Deep Q networks-based optimization of emergency resource scheduling for urban public health events

Research on Urban National Sports Fitness Demand Prediction Method Based on Ant Colony Algorithm

Product Design Scheme Generation and Optimization Decisions While Considering Remanufacturability

Contact Info

Product

Resources

About