An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Yin, Qiang; Cao, Ben; Li, Xue; Wang, Bin; Zhang, Qiang; Wei, Xiaopeng

doi:10.3390/ijms21062191

Cited by 36 publications

(21 citation statements)

References 61 publications

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“…Moreover, in their work [33], the authors predicted the slope stability by utilizing HHO and k-fold cross-validation. Yin et al proposed a new control parameter method to HHO and random OBL to construct DNA storage [18]. Recently, the authors in [34] introduced a four strategies technique, including the OBL strategy, to improve HHO transition rules.…”

Section: Related Workmentioning

confidence: 99%

“…Algorithm 2: IHHO algorithm Input: N: population size, T: maximum number of iterations. Output: The rabbit location( ), potential rabbit location ( ) and their fitness values Initialize the population randomly ( = 1,2, … , ) Find the best opposite solutions based on EOBL, then select the fittest solutions, according to Equation (16), Equation 17and Equation (18). While (maximum iteration not reached ( < )) do…”

Section: Improved Harris Hawks Algorithm (Ihho)mentioning

confidence: 99%

“…The following are some examples of SI algorithms: GA [10], Grasshopper Optimization Algorithm (GOA) [11], Particle Swarm Optimization (PSO) [12], Ant Lion Optimizer (ALO) [13], Whale Optimization Algorithm (WOA) [14], Butterfly Optimization Algorithm (BOA) [15], Slime Mould Algorithm (SMA) [16] and Harris Hawk Optimization (HHO) [17]. Due to the stochastic nature of SI algorithms, it is commonly utilized to solve large space and complex optimization problems [18] .…”

Section: Introductionmentioning

confidence: 99%

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Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

et al. 2020

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The rapid increase in data volume and features dimensionality have a negative influence on machine learning and many other fields, such as decreasing classification accuracy and increasing computational cost. Feature selection technique has a critical role as a preprocessing step in reducing these issues. It works by eliminating the features that may negatively influence the classifiers' performance, such as irrelevant, redundant and less informative features. This paper aims to introduce an improved Harris hawks optimization (IHHO) by utilizing elite oppositebased learning and proposing a new search mechanism. Harris hawks optimization (HHO) is a novel metaheuristic general-purpose algorithm recently introduced to solve continuous search problems. Compared to conventional HHO, the proposed IHHO can avoid trapping in local optima and has an enhanced search mechanism, relying on mutation, mutation neighborhood search, and rollback strategies to raise the search capabilities. Moreover, it improves population diversity, computational accuracy, and accelerates convergence rate. To evaluate the performance of IHHO, we conducted a series of experiments on twenty benchmark datasets collected from the UCI repository and the scikit-feature project. The datasets represent different levels of feature dimensionality, such as low, moderate, and high. Further, four criteria were adopted to determine the superiority of IHHO: classification accuracy, fitness value, number of selected features, and statistical tests. Furthermore, a comparison between IHHO and other well-known algorithms such as Generic algorithm (GA), Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA) and Slime Mould Algorithm (SMA) was performed. The experimental results have confirmed the dominance of IHHO over the other optimization algorithms in different aspects, such as accuracy, fitness value, and feature selection.

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Section: Related Workmentioning

confidence: 99%

Section: Improved Harris Hawks Algorithm (Ihho)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

et al. 2020

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“…In 2020, to decrease the error rate, Cao et al (2020) presented a new constraint, namely, uncorrelated address, and constructed a set of effective DNA codes. Yin et al (2020) considered multiple constraints to ensure accurate hybridization of DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

Wei

Wang

et al. 2021

Front. Genet.

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DNA computing is a new method based on molecular biotechnology to solve complex problems. The design of DNA sequences is a multi-objective optimization problem in DNA computing, whose objective is to obtain optimized sequences that satisfy multiple constraints to improve the quality of the sequences. However, the previous optimized DNA sequences reacted with each other, which reduced the number of DNA sequences that could be used for molecular hybridization in the solution and thus reduced the accuracy of DNA computing. In addition, a DNA sequence and its complement follow the principle of complementary pairing, and the sequence of base GC at both ends is more stable. To optimize the above problems, the constraints of Pairing Sequences Constraint (PSC) and Close-ending along with the Improved Chaos Whale (ICW) optimization algorithm were proposed to construct a DNA sequence set that satisfies the combination of constraints. The ICW optimization algorithm is added to a new predator–prey strategy and sine and cosine functions under the action of chaos. Compared with other algorithms, among the 23 benchmark functions, the new algorithm obtained the minimum value for one-third of the functions and two-thirds of the current minimum value. The DNA sequences satisfying the constraint combination obtained the minimum of fitness values and had stable and usable structures.

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“…Since Dr. Adleman of the United States used DNA molecular biological computing to solve the directed path problem of seven nodes [ 1 ] in 1994, DNA computing has attracted the attention of researchers across the world [ 2 – 5 ]. In DNA computing, DNA nucleotide bases A, C, G, and T coding sequences are used as carriers of information; it has great advantages in dealing with large storage capacity, parallelism, and energy consumption of information [ 6 – 9 ]. DNA computing essentially uses biochemical experiments to address practical problems.…”

Section: Introductionmentioning

confidence: 99%

New insights into the existing image encryption algorithms based on DNA coding

Xue

Zhou

2020

PLoS ONE

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Because a DNA nucleotide sequence has the characteristics of large storage capacity, high parallelism, and low energy consumption, DNA cryptography is favored by information security researchers. Image encryption algorithms based on DNA coding have become a research hotspot in the field of image encryption and security. In this study, based on a comprehensive review of the existing studies and their results, we present new insights into the existing image encryption algorithms based on DNA coding. First, the existing algorithms were summarized and classified into five types, depending on the type of DNA coding: DNA fixed coding, DNA dynamic coding, different types of base complement operation, different DNA sequence algebraic operations, and combinations of multiple DNA operations. Second, we analyzed and studied each classification algorithm using simulation and obtained their advantages and disadvantages. Third, the DNA coding mechanisms, DNA algebraic operations, and DNA algebraic combination operations were compared and discussed. Then, a new scheme was proposed by combining the optimal coding mechanism with the optimal DNA coding operation. Finally, we revealed the shortcomings of the existing studies and the future direction for improving image encryption methods based on DNA coding.

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An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

Cited by 36 publications

References 61 publications

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection

Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

New insights into the existing image encryption algorithms based on DNA coding

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