Utilization of Soft Computing for Evaluating the Performance of Stone Sawing Machines, Iranian Quarries

The escalating construction industry has led to a drastic increase in the dimension stone demand in the construction, mining and industry sectors. Assessment and investigation of mining projects and stone processing plants such as sawing machines is necessary to manage and respond to the sawing performance; hence, the soft computing techniques were considered as a challenging task due to stochastic optimization of this issue and to handle complex optimization problems. In this study, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms as soft computing techniques were used to classify the dimension stones based on physical and mechanical properties and ampere consumption. For this purpose, varieties of dimension stones from 12 quarries located in Iran were investigated. Studied dimension stones were classified into two and three separate clusters using the optimization clustering techniques. The results showed that the applied soft computing technique makes it possible to evaluate the performance of sawing machines in different complex conditions and uncertain systems.

[1]  Tarun Kumar Sharma,et al.  Some modifications to enhance the performance of Artificial Bee Colony , 2012, 2012 IEEE Congress on Evolutionary Computation.

[2]  H. Askari-Nasab,et al.  Automatic creation of mining polygons using hierarchical clustering techniques , 2013, Journal of Mining Science.

[3]  Dervis Karaboga,et al.  A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems , 2011, Appl. Soft Comput..

[4]  Sami Shaffiee Haghshenas,et al.  Utilization of Soft Computing for Risk Assessment of a Tunneling Project Using Geological Units , 2016 .

[5]  A. Ghanbarzadeh,et al.  Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand est , 2010 .

[6]  Alper Ünler,et al.  Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 , 2008 .

[7]  Naveen Dandu,et al.  Optimization of Benchmark Functions using Chemical Reaction Optimization , 2013 .

[8]  L. Durlofsky,et al.  Application of a particle swarm optimization algorithm for determining optimum well location and type , 2010 .

[9]  Ebrahim Kamrani,et al.  Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO) , 2010 .

[10]  T. R. Neelakantan,et al.  Design of water distribution networks using particle swarm optimization , 2006 .

[11]  Peng-Sheng You,et al.  An efficient computational approach for railway booking problems , 2008, Eur. J. Oper. Res..

[12]  Douglas B. Kell,et al.  The landscape adaptive particle swarm optimizer , 2008, Appl. Soft Comput..

[13]  Kejun Zhu,et al.  Energy demand projection of China using a path-coefficient analysis and PSO–GA approach , 2012 .

[14]  Qinghai Bai,et al.  Analysis of Particle Swarm Optimization Algorithm , 2010, Comput. Inf. Sci..

[15]  Mauro Dell'Orco,et al.  Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings , 2014 .

[16]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[17]  Sami Shaffiee Haghshenas,et al.  Development of Intelligent Systems to Predict Diamond Wire Saw Performance , 2017 .

[18]  Reza Mikaeil,et al.  Estimation of the Ampere Consumption of Dimension Stone Sawing Machine Using of Artificial Neural Networks , 2016 .

[19]  Idel Montalvo,et al.  Design optimization of wastewater collection networks by PSO , 2008, Comput. Math. Appl..

[20]  Dharmender Kumar,et al.  Optimization of Benchmark Functions Using Artificial Bee Colony (ABC) Algorithm , 2013 .

[21]  Mohammad Ataei,et al.  Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique , 2018, Neural Computing and Applications.

[22]  Mustafa Sonmez,et al.  Artificial Bee Colony algorithm for optimization of truss structures , 2011, Appl. Soft Comput..

[23]  Mohammad Ataei,et al.  Application of harmony search algorithm to evaluate performance of diamond wire saw , 2019 .

[24]  Dušan Teodorović,et al.  Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .

[25]  Idel Montalvo,et al.  Particle Swarm Optimization applied to the design of water supply systems , 2008, Comput. Math. Appl..

[26]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[27]  Jui-Sheng Chou,et al.  Nature-Inspired Metaheuristic Regression System: Programming and Implementation for Civil Engineering Applications , 2016, J. Comput. Civ. Eng..

[28]  Sami Shaffiee Haghshenas,et al.  - 309-A New Model for Evaluating the Geological Risk Based on Geomechanical Properties — Case Study : The Second Part of Emamzade Hashem Tunnel , 2017 .

[29]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[30]  P. J. Pawar,et al.  Modelling and optimization of process parameters of wire electrical discharge machining , 2009 .

[31]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[32]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[33]  Alok Singh,et al.  An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem , 2009, Appl. Soft Comput..

[34]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[35]  Junjie Li,et al.  Structural inverse analysis by hybrid simplex artificial bee colony algorithms , 2009 .

[36]  Xianjia Wang,et al.  An evolutionary game based particle swarm optimization algorithm , 2008 .

[37]  Kwok-wing Chau,et al.  Particle Swarm Optimization Training Algorithm for ANNs in Stage Prediction of Shing Mun River , 2006 .

[38]  Sami Shaffiee Haghshenas,et al.  The Risk Assessment of Dam Construction Projects Using Fuzzy TOPSIS (Case Study: Alavian Earth Dam) , 2016 .

[39]  Hossain Poorzahedy,et al.  Application of particle swarm optimization to transportation network design problem , 2011 .