Opposition Based Genetic Algorithm with Cauchy Mutation for Function Optimization

Iqbal, Muhammad; Khan, Naveed Kazim; Jaffar, M. Arfan; Ramzan, Muhammad; Baig, Abdul Rauf

doi:10.1109/icisa.2010.5480382

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…The benefit of this strategy is the best solution will never be lost and also there is a chance for getting better solution. The proposed mutation-based Bees Algorithm (mBA) uses four different mutation operators [38,39] which are as follows.…”

Section: Proposed Modelmentioning

confidence: 99%

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Saif

Das

Biswas

2021

J. Inst. Eng. India Ser. B

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In India, the first confirmed case of novel corona virus (COVID-19) was discovered on January 30, 2020. The number of confirmed cases is increasing day by day, and it crossed 21,53,010 on August 9, 2020. In this paper, a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed model is based on adaptive neuro-fuzzy inference system (ANFIS) and mutation-based Bees Algorithm (mBA). The meta-heuristic Bees Algorithm (BA) has been modified applying 4 types of mutation, and mutation-based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USA, and the number of confirmed cases in the next 10 days in India has been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results show that the proposed model has achieved better performance in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE). It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data. Similarly, for USA the values are 4468.72, 3082.07, 6.1, and 0.000952 for RMSE, MAE, MAPE, and NRMSE, respectively. Keywords Mutation-based Bees Algorithm (mBA) Á Adaptive neuro-fuzzy inference system (ANFIS) Á COVID-19 Á Forecast Á RMSE Á MAE Á MAPE Á NRMSE Á India & Suparna Biswas

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Section: Proposed Modelmentioning

confidence: 99%

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Saif

Das

Biswas

2021

J. Inst. Eng. India Ser. B

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“…From the study, these algorithms were adopted with OBL and their improvement were proven from their original algorithms. Amid of OBL algorithm are Opposite-based GA (OGA) [10], Oppositional-based PSO (OPSO) [11], Opposition-based SDA (OSDA) [12] and Opposition-based Simulated Annealing (OSA) [13].…”

mentioning

confidence: 99%

Quasi Oppositional—Manta Ray Foraging Optimization and Its Application to PID Control of a Pendulum System

Razak

Nasir

Rizal

et al. 2021

Lecture Notes in Electrical Engineering

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This paper presents an improved version of Manta Ray Foraging Optimization (MRFO). MRFO is relatively a single objective optimization algorithm. It was inspired from the behavior of a cartilaginous fish called Manta Ray. Manta Ray applies three strategies in searching foods which are chain, cyclone and somersault foraging. From the study, MRFO is a relatively new developed algorithm and has low convergence rate. However, MRFO has potential to be improved in that aspect. In the meanwhile, Opposition-based Learning (OBL) is a well-known technique in increasing the convergence rate. Therefore, a type of OBL namely Quasi Oppositional-based Learning will be adopted into MRFO in order to increase the possibility of finding the solution by considering the opposite individual location of fitness. This version of MRFO is called as Oppositional-based MRFO (OMRFO). Further, OMRFO was performed on several benchmark function. A statistical non-parametric Wilcoxon Test was conducted to analyze the accuracy of MRFO and OMRFO. Furthermore, the proposed algorithm was applied to an inverted pendulum system. Result from shows that performance of OMRFO is significantly outperformed MRFO after tested in the benchmark functions.

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A review of opposition-based learning from 2005 to 2012

Wang

et al. 2014

Engineering Applications of Artificial Intelligence

172

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Opposition Based Genetic Algorithm with Cauchy Mutation for Function Optimization

Cited by 4 publications

References 8 publications

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

Quasi Oppositional—Manta Ray Foraging Optimization and Its Application to PID Control of a Pendulum System

A review of opposition-based learning from 2005 to 2012

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