Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

Marzi, Hosein; Darwish, Ahmed Haj; Helfawi, Humam

doi:10.1080/10798587.2016.1196880

Cited by 17 publications

(7 citation statements)

References 31 publications

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“…The (BA) is a population-based algorithm that solves complicated problems by simulating the behavior of honey bees [51]. In an optimization problem, the BA finds the solution that is closest to the best [52]. Local and global searches are carried out by the BA.…”

Section: Bees Colonymentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

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In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

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Section: Bees Colonymentioning

confidence: 99%

A review: On bio-inspired optimization methods for path planning of mobile robot

Hason

Al-Darraji

2023

IJICI

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“…The gradient-based methods are usually used to adjust the antecedent and consequent parameters in the ANFIS model [55]. One of the issues with the gradient-based methods is that the answer is placed in local optimality, and convergence rate is slow [56,57]. Metaheuristic optimization algorithms, such as particle swarm optimization (PSO) or the genetic algorithm (GA), can be utilized as an effective solution for the issues relating to the gradient-based methods [58,59,60].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

et al. 2019

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The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

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“…While these methods have demonstrated satisfactory results in predicting rockburst conditions, they are not without limitations. For instance, ANN may suffer a slow learn-ing rate and the risk of getting trapped in local minima [24,25,40]; ANFIS can be timeconsuming, due to the need for tuning optimal functions and rules [24,26,41,42]; SVM classifiers require extensive computations and storage, while the KNN algorithm can be computationally intensive [24,35,43,44]. Despite the existence of numerous methods for predicting rockburst conditions and their respective accuracies, developing a reliable and precise method for rockburst-prone zones remains a challenge [24].…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree for Rockburst Conditions Prediction

et al. 2023

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This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.

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Training ANFIS Using the Enhanced Bees Algorithm and Least Squares Estimation

Cited by 17 publications

References 31 publications

A review: On bio-inspired optimization methods for path planning of mobile robot

A review: On bio-inspired optimization methods for path planning of mobile robot

Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

A Decision Tree for Rockburst Conditions Prediction

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