scholarly journals An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

2019 ◽  
Vol 76 ◽  
pp. 629-637 ◽  
Author(s):  
Qu Wei ◽  
Zhaoxia Guo ◽  
Hoong Chuin Lau ◽  
Zhenggang He
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Waste Collection ◽  
Bee Colony

scholarly journals Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Edgar J. Amaya ◽  
Alberto J. Alvares
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Remaining Useful Life ◽  
Data Repository ◽  
Echo State Network ◽  
Health State ◽  
Future Health ◽  
Large Space ◽  
Abc Algorithm ◽  
Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.

scholarly journals Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mustafa Serter Uzer ◽  
Nihat Yilmaz ◽  
Onur Inan
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Bee Colony ◽  
Vector Machines

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

A hybrid approach to artificial bee colony algorithm

2015 ◽  
Vol 27 (2) ◽  
pp. 387-409 ◽  
Author(s):  
Lianbo Ma ◽  
Yunlong Zhu ◽  
Dingyi Zhang ◽  
Ben Niu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Bee Colony

scholarly journals A Hybrid Approach of ANFIS—Artificial Bee Colony Algorithm for Intelligent Modeling and Optimization of Plasma Arc Cutting on Monel™ 400 Alloy

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6373
Author(s):  
Mahalingam Siva Kumar ◽  
Devaraj Rajamani ◽  
Emad Abouel Nasr ◽  
Esakki Balasubramanian ◽  
Hussein Mohamed ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Cutting Speed ◽  
Gas Pressure ◽  
Plasma Arc ◽  
Plasma Arc Cutting ◽  
Response Characteristics ◽  
Bee Colony ◽  
Arc Current

This paper focusses on a hybrid approach based on genetic algorithm (GA) and an adaptive neuro fuzzy inference system (ANFIS) for modeling the correlation between plasma arc cutting (PAC) parameters and the response characteristics of machined Monel 400 alloy sheets. PAC experiments are performed based on box-behnken design methodology by considering cutting speed, gas pressure, arc current, and stand-off distance as input parameters, and surface roughness (Ra), kerf width (kw), and micro hardness (mh) as response characteristics. GA is efficaciously utilized as the training algorithm to optimize the ANFIS parameters. The training, testing errors, and statistical validation parameter results indicated that the ANFIS learned by GA outperforms in the forecasting of PAC responses compared with the results of multiple linear regression models. Besides that, to obtain the optimal combination PAC parameters, multi-response optimization was performed using a trained ANFIS network coupled with an artificial bee colony algorithm (ABC). The superlative responses, such as Ra of 1.5387 µm, kw of 1.2034 mm, and mh of 176.08, are used to forecast the optimum cutting conditions, such as a cutting speed of 2330.39 mm/min, gas pressure of 3.84 bar, arc current of 45 A, and stand-off distance of 2.01 mm, respectively. Furthermore, the ABC predicted results are validated by conducting confirmatory experiments, and it was found that the error between the predicted and the actual results are lower than 6.38%, indicating the adoptability of the proposed ABC in optimizing real-world complex machining processes.

scholarly journals A novel hybrid approach based artificial bee colony and salp swarm algorithms for solving ORPD problem

2021 ◽  
Vol 23 (3) ◽  
pp. 1825
Author(s):  
Souheil Salhi ◽  
Djemai Naimi ◽  
Ahmed Salhi ◽  
Saleh Abujarad ◽  
Abdelouahab Necira
Keyword(s):  
Artificial Bee Colony ◽  
Reactive Power ◽  
Electrical Power ◽  
Hybrid Approach ◽  
Stable State ◽  
Search Space ◽  
Electrical Power System ◽  
Solution Quality ◽  
Abc Algorithm ◽  
Bee Colony

Optimal reactive power dispatch (ORPD) is an important task for achieving more economical, secure and stable state of the electrical power system. It is expressed as a complex optimization problem where many meta-heuristic techniques have been proposed to overcome various complexities in solving ORPD problem. A meta-heuristic search mechanism is characterized by exploration and exploitation of the search space. The balance between these two characteristics is a challenging problem to attain the best solution quality. The artificial bee colony (ABC) algorithm as a reputed meta-heuristic has proved its goodness at exploration and weakness at exploitation where the enhancement of the basic ABC version becomes necessary. Salp swarm algorithm (SSA) is a newly developed swarm-based meta-heuristic, which has the best local search capability by using the best global solution in each iteration to discover promising solutions. In this paper, a novel hybrid approach-based ABC and SSA algorithms (ABC-SSA) is that developed to enhance the exploitation capability of the ABC algorithm using SSA and applied for solving ORPD problem. The efficiency of ABC-SSA is investigated using two standard test systems IEEE-30 and IEEE-300 buses, and that by considering the famous objective functions in ORPD problem.

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