An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization

Zhao, Xiaoqiang; Yang, Fan; Han, Yazhou; Cui, Yanpeng

doi:10.1109/access.2020.2976101

Cited by 43 publications

(22 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…By analyzing achieved performance metrics, it is concluded that the exploitation process and also the balance between exploration and intensification can be better adjusted. First, the proposed improved BSO approach incorporates chaotic local search (CLS) in the initialization phase, similar to the method used in [28]. The search process has been modified by introducing the CLS approach, which helps to improve the performances of BSO in achieving the global optimum.…”

Section: Observed Deficiencies Of Basic Bso and Proposed Enhanced Metaheuristicsmentioning

confidence: 99%

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Bačanin¹,

Alhazmi²,

Živković³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing gradient issue. As an alternative approach, stochastic optimization algorithms, such as nature-inspired metaheuristics are more reliable for complex optimization tax, such as finding the proper values of weights and biases for neural network training. In this work, we propose an enhanced brain storm optimization-based algorithm for training neural networks. In the simulations, ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm. The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed, due to the capability of balancing the intensification and diversification and avoiding the local minima. The proposed approach obtained the best accuracy on eight out of ten observed dataset, outperforming all other algorithms by 1-2% on average. When mean accuracy is observed, the proposed algorithm dominated on nine out of ten datasets.

show abstract

Section: Observed Deficiencies Of Basic Bso and Proposed Enhanced Metaheuristicsmentioning

confidence: 99%

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Bačanin¹,

Alhazmi²,

Živković³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…erefore, in the past few decades, chaos theory has been used in many fields such as parameter optimization, feature selection, and chaos control [3]. In recent years, chaotic mapping has also become a widely popular method to improve metaheuristic algorithms, such as chaotic parameter control [32,33], chaotic initialization [34][35][36], and chaotic local search [37][38][39]. In this study, the logistic chaos map is utilized as a chaotic sequence.…”

Section: Chaotic Initialization Strategy (Cis)mentioning

confidence: 99%

“…Ibrahim et al used the chaotic map to process the original solution to raise the convergence of the GWO [36]. Zhao et al [37] used the CLS to enhance the performance of SSA. Chen et al [38] proposed an enhanced BFO with CLS.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design

Liang

Liu³

et al. 2021

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

In the past few decades, metaheuristic algorithms (MA) have been developed tremendously and have been successfully applied in many fields. In recent years, a large number of new MA have been proposed. Slime mould algorithm (SMA) is a novel swarm-based intelligence optimization algorithm. SMA solves the optimization problem by imitating the foraging and movement behavior of slime mould. It can effectively obtain a promising global optimal solution. However, it still suffers some shortcomings such as the unstable convergence speed, the imprecise search accuracy, and incapability of identifying a local optimal solution when faced with complicated optimization problems. With the purpose of overcoming the shortcomings of SMA, this paper proposed a multistrategy enhanced version of SMA called ESMA. The three enhanced strategies are chaotic initialization strategy (CIS), orthogonal learning strategy (OLS), and boundary reset strategy (BRS). The CIS is used to generate an initial population with diversity in the early stage of ESMA, which can increase the convergence speed of the algorithm and the quality of the final solution. Then, the OLS is used to discover the useful information of the best solutions and offer a potential search direction, which enhances the local search ability and raises the convergence rate. Finally, the BRS is used to correct individual positions, which ensures the population diversity and enhances the overall search capabilities of ESMA. The performance of ESMA was validated on the 30 IEEE CEC2014 functions and three IIR model identification problems, compared with other nine well-regarded and state-of-the-art algorithms. Simulation results and analysis prove that the ESMA has a superior performance. The three strategies involved in ESMA have significantly improved the performance of the basic SMA.

show abstract

“…A recent bio-inspired algorithm inspired from the swarm behavior of salp commonly termed as SSA is adopted in this research for both feature selection and classification. The advantages of SSA are few parameters requirement, low computational cost and simple implementation [16]. The convergence speed of traditional SSA is enhanced by integrating opposition based learning (OBL) strategy at the initialization stage and named as Improved SSA (ISSA) [17].…”

Section: Introductionmentioning

confidence: 99%

“…Attack Detection rate (ADR): It is also called as sensitivity or recall. It calculates the capability of the attack detection as denoted in Equation(16). It is the number of correctly detected attack instances over classified attack instances.…”

mentioning

confidence: 99%

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

Prakash

2021

Preprint

View full text Add to dashboard Cite

Internet of Things (IoT) is the powerful latest trend that allows communications and networking of many sources over the internet. Routing protocol for low power and lossy networks (RPL) based IoT networks may be exposed to many routing attacks due to resource-constrained and open nature of the IoT nodes. Hence, there is a need for network intrusion detection system (NIDS) to protect RPL based IoT networks from routing attacks. The existing techniques for anomaly-based NIDS (ANIDS) subjects to high false alarm rate (FAR). Therefore, a novel bio-inspired voting ensemble classifier with feature selection technique is proposed in this paper to improve the performance of ANIDS for RPL based IoT networks. The proposed voting ensemble classifier combines the results of various base classifiers such as logistic Regression, support vector machine, decision tree, bidirectional long short-term memory and K-nearest neighbor to detect the attacks accurately based on majority voting rule. The optimized weights of base classifiers are obtained by using the feature selection method called simulated annealing based improved salp swarm algorithm (SA-ISSA), which is the hybridization of particle swarm optimization, opposition based learning and salp swarm algorithm. The experiments are performed with RPL-NIDDS17 dataset that contains seven types of attack instances. The performance of the proposed model is evaluated and compared with existing feature selection and classification techniques in terms of accuracy, attack detection rate (ADR), FAR and so on. The proposed ensemble classifier shows better performance with higher accuracy (96.4%), ADR (97.7%) and reduced FAR (3.6%).

show abstract

An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization

Cited by 43 publications

References 34 publications

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

An Enhanced Slime Mould Algorithm and Its Application for Digital IIR Filter Design

A Novel Intrusion Detection System for RPL Based IoT Networks with Bio-Inspired Feature Selection and Ensemble Classifier

Contact Info

Product

Resources

About