scholarly journals Fuzzy Dynamic Parameter Adaptation in the Bird Swarm Algorithm for Neural Network Optimization

2021 ◽  
Author(s):  
Patricia Melin ◽  
Ivette Miramontes ◽  
Oscar Carvajal ◽  
German Prado-Arechiga
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Statistical Tests ◽  
Systolic Pressure ◽  
Dynamic Parameter ◽  
Real Problem ◽  
Parameter Adaptation ◽  
Original Algorithm ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm

Abstract Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm's performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses patient information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.

scholarly journals The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 36078-36086
Author(s):  
Chengchang Zhang ◽  
Sa Yu ◽  
Guojun Li ◽  
Yu Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Method ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm

Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation

2020 ◽  
Vol 11 (4) ◽  
pp. 46-59
Author(s):  
Scaria Alex ◽  
T. Dhiliphan Rajkumar
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Web Applications ◽  
Real Life ◽  
Web Pages ◽  
Security Issue ◽  
Deep Recurrent Neural Network ◽  
Swarm Algorithm ◽  
Scripting Language ◽  
Bird Swarm Algorithm

JavaScript is a scripting language that is commonly used in the web pages for providing dynamic functionality in order to enhance user experience. Malicious JavaScript in webpages on internet is an important security issue due to their potentially and universality severe impact. Finding the malicious JavaScript is usually more difficult and time-consuming task in the research community. Hence, an adaptive spider bird swarm algorithm-based deep recurrent neural network (adaptive SBSA-based deep RNN) is proposed for detecting the malicious JavaScript codes in web applications. However, the proposed adaptive SBSA is designed by integrating the adaptive concept with the bird swarm algorithm (BSA) and spider monkey optimization (SMO). With the deep RNN classifier, the complexity issues exists in detecting the malicious codes is effectively resolved through the process of hierarchical computation. Due to the efficiency of the proposed approach, it can evaluate under large real-life datasets.

A high-speed interval type 2 fuzzy system approach for dynamic parameter adaptation in metaheuristics

2019 ◽  
Vol 85 ◽  
pp. 666-680 ◽  
Author(s):  
Oscar Castillo ◽  
Patricia Melin ◽  
Emanuel Ontiveros ◽  
Cinthia Peraza ◽  
Patricia Ochoa ◽  
...  
Keyword(s):  
Fuzzy System ◽  
System Approach ◽  
High Speed ◽  
Dynamic Parameter ◽  
Parameter Adaptation ◽  
Interval Type

scholarly journals High-Speed Interval Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search for Optimal Design of Fuzzy Controllers

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 758
Author(s):  
Oscar Castillo ◽  
Fevrier Valdez ◽  
Cinthia Peraza ◽  
Jin Hee Yoon ◽  
Zong Woo Geem
Keyword(s):  
Fuzzy System ◽  
Fuzzy Systems ◽  
Random Noise ◽  
Computing Time ◽  
Harmony Search ◽  
Computational Cost ◽  
Dynamic Parameter ◽  
Parameter Adaptation ◽  
Interval Type

Fuzzy systems have become a good solution to the problem of fixed parameters in metaheuristic algorithms, proving their efficiency when performing dynamic parameter adaptations using type-1 and type-2 fuzzy logic. However, the computational cost of type-2 fuzzy systems when using the continuous enhanced Karnik–Mendel (CKM) algorithm for type-reduction, when applied to control and optimization, is too high. Therefore, it is proposed to use an approximation to the CKM algorithm in the type-2 fuzzy system for adjusting the pitch adjustment rate (PArate) parameter in the original harmony search algorithm (HS). The main contribution of this article is to verify that the implementation of the proposed methodology achieves results that are equivalent to the interval type-2 fuzzy system with the CKM algorithm, but in less computing time and also allowing an efficient dynamic parameter adaptation. It is noteworthy that this method is relatively new in the area of metaheuristics algorithms so there is a current interest to work with this methodology. The proposed method was used in optimizing the antecedents and consequents for an interval type-2 fuzzy controller of direct current motor. Experimental results without noise and then with uniform random noise numbers (Gaussian noise) in the controller were obtained to verify that the implementation is efficient when compared to conventional and other existing methods.

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