NBBO: A new variant of biogeography-based optimization with a novel framework and a two-phase migration operator

Reihanian, Ali; Feizi‐Derakhshi, Mohammad‐Reza; Aghdasi, Hadi S.

doi:10.1016/j.ins.2019.07.054

Cited by 18 publications

(7 citation statements)

References 38 publications

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“…Mutation operation is another important operation of BBO. After migration operation, mutation operation randomly changes SIV in some habitats according to the mutation rate [27] . As the SIV value changes, the number of species in the habitat will be changed.…”

Section: Methodsmentioning

confidence: 99%

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

Sun

Tang

et al. 2021

Knowledge-Based Systems

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Aim: By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor. Method: To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks. Results: Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models. Conclusion: Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.

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Section: Methodsmentioning

confidence: 99%

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

Sun

Tang

et al. 2021

Knowledge-Based Systems

View full text Add to dashboard Cite

show abstract

“…However, the step size of gaussian distributed random number is short, which can not greatly help the algorithm to escape from the local optimal solution. In addition, NBBO [16], EMBBO [10] and WRBBO [53] directly delete the mutation operator to avoid random mutation generating inferior solutions. Although the computation is reduced, the algorithm only relies on the migration operator to search new solutions, which has the problem of slow convergence speed, and the population diversity decreases rapidly, and the algorithm is easy to fall into the local optimal state.…”

Section: Algorithmmentioning

confidence: 99%

“…In OBBO, the opposite individuals are merged into BBO population to improve the diversity, and the optimization performance of this algorithm is obviously better than that of standard BBO. Recently, Reihanian et al [16] introduced a new two-stage migration operator in the framework of BBO to enable the algorithm to search the problem space effectively. For the mutation operator, the harmony search (HS) [18] process was added to the mutation operator of BBO in literature [17], and HSBBO was obtained.…”

Section: Introductionmentioning

confidence: 99%

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

2022

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Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator. To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation, named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used to improve the initial population ergodicity. Secondly, a nonlinear transformation parameter and a inertia weight adjustment factor are designed into the position update formula of SCA to make SCBBO suitable for high dimensional environments. Then, a dynamic hybrid mutation operator is designed by combining Laplacian and Gaussian mutation, which helps the algorithm to escape from local optima and balance the exploration and exploitation. Finally, the dual learning strategy is integrated, so the convergence accuracy is further improved by generating dual individuals. Meanwhile, A sequence convergence model is established to prove the algorithm can converge to the global optimal solution with probability 1. Compared with other state-of-the-art evolutionary algorithms, SCBBO effectively improves the optimization accuracy and convergence speed for high-dimensional optimization problems. To further show the superiority of SCBBO, the performance is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. The comparsions show that SCBBO's optimization results on these dimensions are basically the same. SCBBO also applied to engineering design problems, and the simulation results demonstrate that the proposed method is also effective on constrained optimization problems.

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“…For the prediction of landslide susceptibility, Abolfazl Jaafari et al [40] used Meta optimization with GWO and BBO algorithms. To remove the inability in BBO and make a better exploration and exploitation properties, Ali Reihanian et al [41] introduced NBBO, a new variant of BBO. Bo Yang et al [42] used BBO as an optimization tool with consensus dynamics for community detection.…”

Section: Where: T=current Iteration T=maximum Iterationmentioning

confidence: 99%

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Sharma

Saxena²,

Palwalia

2022

Technol Econ Smart Grids Sustain Energy

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Demand Response (DR) keeps up an important character in the growth of electricity generation with transmission and distribution in the phase of advanced grid automation. For future smart grid, basically two types of Demand Side Management strategies have been used with a number of shiftable or controllable devices. In this research article, two new variants of Biogeography Based Optimization (BBO) algorithm named as Iteration Control BBO and Pandemic BBO are proposed for solving the minimization problem and definitions of these variants have been made by change in migration control � ⃗ a vector. These two variants are indirectly support the principle of No Free Lunch theorem. The beauty of these variants is that they are reported the impact of divergence in BBO on efficient energy strategy of DSM. The simulation results reported in this paper claims that the used DSM techniques acquire financial savings, while lowering and shifting peak load.

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NBBO: A new variant of biogeography-based optimization with a novel framework and a two-phase migration operator

Cited by 18 publications

References 38 publications

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

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