Modified bat algorithm based on covariance adaptive evolution for global optimization problems

Shan, Xian; Cheng, Huijin

doi:10.1007/s00500-017-2952-5

Cited by 14 publications

(7 citation statements)

References 47 publications

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“…Compared with PSO and GA, BA can avoid local optimization because of its stronger global optimization ability, lower parameters, and higher efficiency. The modified bat algorithm (MBA) further improves the global optimization performance of the bat algorithm by adjusting the search direction and using parameter adaptive strategy …”

Section: Methodsmentioning

confidence: 99%

“…The modified bat algorithm (MBA) further improves the global optimization performance of the bat algorithm by adjusting the search direction and using parameter adaptive strategy. [27] As in the traditional bat algorithm, search for the local search direction toward the best solution. In order to improve the search ability of the algorithm, it is assumed that each bat emits pulses in two different directions.…”

Section: Modified Bat Algorithmmentioning

confidence: 99%

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Chemical process fault diagnosis based on enchanted machine‐learning approach

et al. 2019

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In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F1‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.

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Section: Methodsmentioning

confidence: 99%

Section: Modified Bat Algorithmmentioning

confidence: 99%

Chemical process fault diagnosis based on enchanted machine‐learning approach

et al. 2019

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“…Due to a lack of searching capability, the classic BA has issues with local optimums and premature convergence. Based on the covariance adaptive process, a new BAT variant that is more capable of exploring is proposed [35]. Information in the proposed manner increases the search procedure by varying the search direction and population sampling distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

Ant Lion Optimization based inertia weight Optimized KGMO for Mobility Management in Heterogeneous LTE Cellular Networks

G.K¹,

T²,

Komarolu³

et al. 2022

Preprint

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A Network densification is understood as a critical process in the development of cellular networks because to the exponentially rising demands of mobile data traffic. One possible technology to fulfil the requirements of exploding data traffic in 5G is the Ultra-dense heterogeneous network (UDHN). 5G networks of the future will feature dense networks that can deliver high data rates in crowded urban areas. As a result, in heterogeneous networks, the amount of interference and the number of handovers (HOs) increases as more small cells are deployed. In this case, mobility management is critical to ensure that the user's communication is uninterrupted while they travel between cells. Kinetic Gas Molecular Optimization (KGMO) and Ant Lion Optimizatio (ALO) are presented in this study as a hybrid optimization-based mobility management strategy for enhancing network performance Initially, the particles of the KGMO such as position and velocity of the gas molecule are calculated based on the kinetic energy. However, KGMO offers faster convergence in large dimensional space and this leads to the hybridization of KGMO. To overcome this issues of KGMO, ALO is used by modifying the inertia weight of KGMO. The simulation setup of proposed KGMO-ALO is done in MATLAB environment and validated with other popular meta-heuristic techniques such as Seagull and monarch butterfly optimizations in terms of throughput, Bit error rate, end-to-end delay and HO.

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“…In addition to this directional echo localization, three other improvements are embedded in the standard bat algorithm to improve its performance. In order to improve the search ability of the bat algorithm, an improved bat algorithm based on the covariance adaptive evolution process is proposed [23]. The information contained in the covariance adaptive evolution diversifies the search direction and sampling distribution of the population, which is of great benefit to the search process.…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators

Zhu¹,

Wang²,

Wang³

et al. 2020

IEEE Access

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Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

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Modified bat algorithm based on covariance adaptive evolution for global optimization problems

Cited by 14 publications

References 47 publications

Chemical process fault diagnosis based on enchanted machine‐learning approach

Chemical process fault diagnosis based on enchanted machine‐learning approach

Ant Lion Optimization based inertia weight Optimized KGMO for Mobility Management in Heterogeneous LTE Cellular Networks

Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators

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