Prediction method of mine gas emission based on complex neural work optimized by Wolf pack algorithm

Xu, Liqiang; Wang, Bin-Guo; Du, Xuedong; Hong, Yu

doi:10.1080/21642583.2018.1531081

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…As an emerging swarm intelligence algorithm, the Wolf Pack algorithm exhibits good global convergence and computational robustness in the process of solving complex high-dimensional functions [22,23]. During the hunting process, each wolf is classified into Alpha, Beta, or Omega wolves according to its different roles.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Wei

2022

Scientific Programming

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In logistics distribution systems, the constrained optimisation of the cargo dispensing problem has been the focus of research in related fields. At present, many scholars try to solve the problem by introducing swarm intelligence algorithms, including genetic algorithm, particle swarm algorithm, bee swarm algorithm, fish swarm algorithm, etc. Each swarm intelligence algorithm has different characteristics, but they all have certain advantages for the optimisation of complex problems. In recent years, the Wolf Pack algorithm, an emerging swarm intelligence algorithm, has shown good global convergence and computational robustness in solving complex high-dimensional functions. Therefore, this article chooses to use the Wolf Pack algorithm to solve a multi-vehicle and multi-goods dispensing problem model. First, the principle and process of the Wolf Pack algorithm are introduced, and two improvements are proposed for the way of location update and the way of step update. Then, a mathematical model of the multi-vehicle and multi-goods dispensing problem is developed. Next, the mathematical model is solved using the proposed improved Wolf Pack algorithm. The experimental results show that the proposed improved Wolf Pack algorithm effectively solves the cargo dispatching problem. In addition, the proposed improved Wolf Pack algorithm can effectively reduce the number of vehicles to be dispatched compared with other swarm intelligence algorithms.

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Section: Introductionmentioning

confidence: 99%

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Wei

2022

Scientific Programming

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“…In order to solve the scheduling problems of Re-entrant Hybrid Flowshop (RHFS), Han et al investigated the mathematical programming model of RHFS, and proposed the Wolf Pack Algorithm (WPA) as a global optimization method [11]. In view of the local extreme problem of the gradient descent algorithm which makes the working face of mine gas emission prediction uncertainly, Xu et al combined Wolf Pack Algorithm (WPA) with complex neural network nonlinear prediction method to the established new prediction model [12]. In the literature [13], Gao et al proposed a Quantum-Inspired Wolf Pack Algorithm (QWPA) based on quantum encoding to enhance the performance of the Wolf Pack Algorithm (WPA) to solve the 0-1 knapsack problems.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

Yang

Wang

et al. 2020

IEEE Access

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Software reliability is estimated and predicted based on software reliability model and software failure data. As a new optimization method, swarm intelligence algorithm has been widely used in solving the parameter optimization of the model. WPA (Wolf Pack Algorithm) and PSO (Particle Swarm Optimization) are two typical swarm intelligence algorithms. WPA has a strong global optimization ability, fast convergence speed and various optimization strategies, but the algorithm is relatively complex. PSO algorithm has a simple structure and fast convergence speed, but it is easy to fall into premature, which leads to low accuracy of solution. Considering the advantages and disadvantages of the two algorithms, a hybrid method of WPA and PSO is proposed, and a fitness function is constructed on maximum likelihood estimation, then the parameters of software reliability model are estimated and predicted based on the hybrid algorithm (WPA-PSO). Five sets of data from industry are used to estimate the parameters of GO model and make predictions. The simulation results show that the hybrid algorithm has higher accuracy of parameter estimation, better optimization performance, better accuracy of prediction and algorithm stability than single algorithm, and show obvious advantages than the single algorithm in the case of limited data.

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A data-driven approach for the prediction of coal seam gas content using machine learning techniques

Akdas

Fişne

2023

Applied Energy

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Prediction method of mine gas emission based on complex neural work optimized by Wolf pack algorithm

Cited by 5 publications

References 6 publications

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems

Parameter Estimation of Software Reliability Model and Prediction Based on Hybrid Wolf Pack Algorithm and Particle Swarm Optimization

A data-driven approach for the prediction of coal seam gas content using machine learning techniques

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