Introduction of Biogeography-Based Programming as a new algorithm for solving problems

Golafshani, Emadaldin Mohammadi

doi:10.1016/j.amc.2015.08.026

Cited by 13 publications

(2 citation statements)

References 28 publications

(51 reference statements)

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“…Here, T vt is the number of patterns in the validation and testing part, T t is the number of patterns in the training part, and P d is the number of patterns in the dataset. The experimental analysis' result is R e , and the model result is R m , which is connected to the d th record [23,24]. The network's complexity is defined in this study as the total of its weights and biases, which is calculated as follows:…”

Section: Proposed Modelmentioning

confidence: 99%

Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

et al. 2021

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Machine learning is the discipline of learning commands in the computer machine to predict and expect the results of real application and is currently the most promising simulation in artificial intelligence. This paper aims at using different algorithms to calculate and predict the compressive strength of extrusion 3DP concrete (cement mortar). The investigation is carried out using multi-objective grasshopper optimization algorithm (MOGOA) and artificial neural network (ANN). Given that the accuracy of a machine learning method depends on the number of data records, and for concrete 3D printing, this number is limited to few years of study, this work develops a new method by combining both methodologies into an ANNMOGOA approach to predict the compressive strength of 3D-printed concrete. Some promising results in the iteration process are achieved.

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Section: Proposed Modelmentioning

confidence: 99%

Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

et al. 2021

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“…Ma et al, [12] Analyzed the Biogeography-Based optimization for Binary Problems provide the best candidate in the population from one generation to the next which converges to the global optimum solution. Golafshani [13] explored the impact of the Biogeography Based Programming for several benchmark functions to solve the problems. Weian et al, [14] Investigated migration models for Multi-Objective Problems (MOPs) using BBO.…”

Section: Introductionmentioning

confidence: 99%

Convergence Study of Biogeography Based Optimization

Mishra¹,

Shinde

Wadhwa³

et al. 2018

AJCST

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Biogeography based optimization BBO is a progressive algorithm. It is induced by Biogeography. BBO is more powerful algorithm among the biology based optimization methods. In this paper examines the convergence of BBO algorithm on some fitness functions. BBO algorithm handles the best solution from one off spring to the next converges to the universal optimum. The convergence rate evaluate of BBO algorithm by simulation for some fitness function. A set of 12 standard benchmark function performance of convergence is studied by BBO algorithm.

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Modeling the compressive strength of concrete containing waste glass using multi-objective automatic regression

Golafshani

Kashani

2022

Neural Comput & Applic

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Some grades of municipal and industrial waste glass (WG) discarded in landfills can cause environmental issues. One of the efficient solutions to overcome this issue is to use WG in concrete mixtures as aggregate or supplementary cementitious materials. Modeling the compressive strength (CS) of the produced concrete using machine learning methods can provide helpful insights into the effects of WG on concrete properties. In this study, a comprehensive database of concrete containing WG (CCWG) was gathered from 24 peer-reviewed papers. Two different scenarios were considered in the selection of input variables, and a novel machine learning method, called multi-objective multi-biogeography-based programming, was used to predict the CS of CCWG. This algorithm can automatically select the effective input variables, the structure of the equations, and its coefficients. Moreover, the proposed model optimizes the precision and complexity of the developed models simultaneously. The definition of complexity in the optimization problem can help achieve different mathematical equations with various accuracies and assist users in predicting the CS of CCWG even with a limited number of optimal input variables. The results show that the proposed algorithm can introduce several equations with different accuracies, complexities, and input variables to predict the CS of CCWG.

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Introduction of Biogeography-Based Programming as a new algorithm for solving problems

Cited by 13 publications

References 28 publications

Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning

Convergence Study of Biogeography Based Optimization

Modeling the compressive strength of concrete containing waste glass using multi-objective automatic regression

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