Algorithm of Coal Mine Rescue Robot Model Based on PSO and GEP

2013 ◽  
Vol 416-417 ◽  
pp. 739-742
Author(s):  
Xue Chen Wang ◽  
Xiao Guang Yue
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Coal Mine ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Programming Algorithm ◽  
Swarm Optimization ◽  
Rescue Robot ◽  
Model Based ◽  
Mine Rescue

In order to study a mine rescue robot model, gene expression programming algorithm is studied. The gene expression programming Algorithm can simulate many scientific models, and has been successfully applied in many aspects. Particle swarm optimization algorithm is discussed. Each member of the particle swarm optimization group can study its own experience and other members' experience to continuously change their search mode. Finally, a coal mine rescue robot model based on the gene expression programming and particle swarm optimization is put forward.

Research on Coal Mine Rescue Robot Model

2013 ◽  
Vol 340 ◽  
pp. 801-804
Author(s):  
Xue Chen Wang ◽  
Xiao Guang Yue ◽  
Qing Guo Ren ◽  
Zi Qiang Zhao
Keyword(s):  
Gene Expression ◽  
Coal Mine ◽  
Gene Expression Programming ◽  
Biological Evolution ◽  
Programming Algorithm ◽  
Multi Agent System ◽  
Agent System ◽  
Rescue Robot ◽  
Multi Agent ◽  
Mine Rescue

According to the situation of frequently domestic mining safety accidents, the basic theory and related concepts of bioinformatics' gene expression programming and multi-agent system are discussed. Related concepts of Bioinformatics and biological evolution and evolutionary computation are described in this paper. A coal mine rescue robot working model is discussed based on bioinformatics gene expression programming algorithm and multi-agent system theory.

Shear strength prediction of short circular reinforced-concrete columns using soft computing methods

2020 ◽  
Vol 23 (14) ◽  
pp. 3048-3061
Author(s):  
Hesam Ketabdari ◽  
Farzad Karimi ◽  
Mahsa Rasouli
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Shear Strength ◽  
Particle Swarm Optimization ◽  
Reinforced Concrete ◽  
Gene Expression Programming ◽  
Particle Swarm ◽  
Concrete Columns ◽  
Swarm Optimization ◽  
Reinforced Concrete Columns

In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particle swarm optimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

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