The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production

Edy Tonnizam Mohamad, Diyuan Li, Bhatawdekar Ramesh Murlidhar, Danial Jahed Armaghani, Khairul Anuar Kassim, Ibrahim Komoo

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

As blasting has some environmental constraints, ripping has got more prevalent as a method of breaking ground in both civil and mining engineering applications. A rippability model of a higher applicability is, therefore, needed to appropriately predict the ripping production (Q) prior to carrying out such tests. For the purpose of predicting the results of ripping production that were attained in three sites located in Johor state, Malaysia, the present study applied three hybrid intelligent techniques, i.e., neuro-bee, neuro-imperialism, and neuro-swarm, to predict Q. In fact, the effects of artificial bee colony (ABC), imperialism competitive algorithm (ICA), and particle swarm optimization (PSO) on weights and biases of neural networks were examined in the current research to receive better prediction/evaluation of ripping production. To do this, totally, 74 ripping tests were taken into consideration in the investigated regions and their influential parameters were assessed. Many parametric studies on ABC, ICA, and PSO parameters were conducted, and then, a comparison was done on the obtained results of the predictive hybrid models using several performance indices. As confirmed by the comparative results, the neuro-bee model proposed in this study estimated Q with a higher accuracy than the other hybrid models. The root-mean-square error (RMSE) values of 0.060, 0.076, and 0.094 were obtained for testing the data sets of neuro-bee, neuro-imperialism, and neuro-swarm techniques, respectively. This clearly shows that the newly developed hybrid model was superior to its rival in terms of predicting the ripping production. Furthermore, results of sensitivity analysis showed that weathering zone is the most influential factor on ripping production as compared to other inputs.

Original languageEnglish
JournalEngineering with Computers
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Particle swarm optimization (PSO)
Optimization Techniques
Particle Swarm Optimization
Hybrid Model
Prediction
Swarm
Mining engineering
Predict
Malaysia
Predictive Model
Blasting
Weathering
Performance Index
Civil engineering
Engineering Application
Mean square error
Sensitivity analysis
Sensitivity Analysis
Mining
High Accuracy

Keywords

  • Neuro-bee
  • Neuro-imperialism
  • Neuro-swarm
  • Ripping production
  • Surface excavation

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Engineering(all)
  • Computer Science Applications

Cite this

The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production. / Mohamad, Edy Tonnizam; Li, Diyuan; Murlidhar, Bhatawdekar Ramesh; Jahed Armaghani, Danial; Kassim, Khairul Anuar; Komoo, Ibrahim.

In: Engineering with Computers, 01.01.2019.

Research output: Contribution to journalArticle

Mohamad, Edy Tonnizam ; Li, Diyuan ; Murlidhar, Bhatawdekar Ramesh ; Jahed Armaghani, Danial ; Kassim, Khairul Anuar ; Komoo, Ibrahim. / The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production. In: Engineering with Computers. 2019.
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