A parallel hybrid krill herd algorithm for feature selection

Abualigah, Laith; Alsalibi, Bisan; Shehab, Mohammad; Alshinwan, Mohammad; Khasawneh, Ahmad M.; Alabool, Hamzeh

doi:10.1007/s13042-020-01202-7

Cited by 35 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature selection is the process of determining which features are relevant to or will improve classification accuracy [27]. Furthermore, feature selection includes identifying a subset of the most beneficial features generating consistent outcomes as the whole original set of features.…”

Section: Feature Selectionmentioning

confidence: 99%

A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

Şahin

Abualigah

2021

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

The automatic detection of software vulnerabilities is considered a complex and common research problem. It is possible to detect several security vulnerabilities using static analysis (SA) tools, but comparatively high false-positive rates are observed in this case. Existing solutions to this problem depend on human experts to identify functionality, and as a result, several vulnerabilities are often overlooked. This paper introduces a novel approach for effectively and reliably finding vulnerabilities in open-source software programs. In this paper, we are motivated to examine the potential of the clonal selection theory. A novel deep learning-based vulnerability detection model is proposed to define features using the clustering theory of the clonal selection algorithm. To our knowledge, this is the first time we have used deep-learned longlived team-hacker features to process memories of sequential features and mapping from the entire history of previous inputs to target vectors in theory. With an immune-based feature selection model, the proposed approach aimed to improve static analyses' detection abilities. A real-world SA dataset is used based on three open-source PHP applications. Comparisons are conducted based on using a classification model for all features to measure the proposed feature selection methods' classification improvement. The results demonstrated that the proposed method got significant enhancements, which occurred in the classification accuracy also in the true positive rate.

show abstract

Section: Feature Selectionmentioning

confidence: 99%

A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

Şahin

Abualigah

2021

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compared to traditional approaches, meta-heuristic algorithms (MAs) are often able to obtain the global best results on such problems, which is attributed to the merits of their simple structure, ease of implementation, as well as strong capability to bypass the local optimum [9,10]. As a result, during the past few decades, MAs have entered the blowout stage and received major attention from worldwide scholars [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Xiao¹,

Sun²,

Guo³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting trapped in the local optimum, the lens opposition-based learning mechanism is adopted to expand the search ranges. Besides, a novel local search-based algorithm, namely adaptive β-hill climbing, is amalgamated with GTO to increase the final solution precision. Attributed to three improvements, the exploration and exploitation capabilities of the basic GTO are greatly enhanced. The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions. The numerical and statistical results demonstrate that IGTO can provide better solution quality, local optimum avoidance, and robustness compared with the basic GTO and five other wellknown algorithms. Moreover, the applicability of IGTO is further proved through resolving four engineering design problems and training multilayer perceptron. The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

show abstract

“…Because of the benefits outlined above, population-based metaheuristics are quite popular and frequently employed today. Several MAs have thus been created for use in biomedicine [26], bioinformatics [27], cheminformatics [28], feature selection [29], engineering issues [30], [31], pattern recognition, text clustering [32], and wireless sensor networks [33]. On the other hand, all meta-heuristic (MA) algorithms need to strike the equilibrium between the exploration and exploitation stages.…”

Section: Introductionmentioning

confidence: 99%

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

et al. 2022

View full text Add to dashboard Cite

The Salp Swarm Algorithm (SSA) outperforms well-known algorithms such as particle swarm optimizers and grey wolf optimizers in complex optimization challenges. However, like most meta-heuristic algorithms, SSA suffers from slow convergence and stagnation in the best local solution. In this study, a Salp swarm algorithm (SSA) is combined with a local escaping operator (LEO) to overcome some inherent limitations of the original SSA. SSALEO is a novel search technique that accounts for population diversity, the imbalance between exploitation and exploration, and the SSA algorithm's premature convergence. By implementing LEO in SSALEO, the search slowdown in SSA is eliminated, and the local search efficiency of swarm agents is improved. The proposed SSALEO method is tested using the CEC 2017 benchmark with 50 and 100 decision variables, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to other metaheuristic algorithms (MAs) and advanced algorithms, including seven Salp swarm variants. The comparisons show that SSA greatly benefits from LEO by enhancing the quality and accelerating its solutions' convergence rate. The SSALEO was then assessed using a benchmark set of seven well-known constrained design challenges in various engineering domains defined in the CEC 2020 conference benchmark. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. ACCORDING TO EXPERIMENTAL DATA AND STATISTICAL TESTS, the SSALEO algorithm is very competitive and often superior to the algorithms used in the studies. Further, the proposed approach can be viewed as a special LSGO optimizer whose performance exceeds that of specialized state-of-the-art algorithms like CMA-ES and SHADE.

show abstract

A parallel hybrid krill herd algorithm for feature selection

Cited by 35 publications

References 43 publications

A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Comparing SSALEO as a Scalable Large Scale Global Optimization Algorithm to High-Performance Algorithms for Real-World Constrained Optimization Benchmark

Contact Info

Product

Resources

About