A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

Chaves-González, José M.; Vega‐Rodríguez, Miguel A.; Granado-Criado, José M.

doi:10.1016/j.engappai.2013.04.011

Cited by 28 publications

(5 citation statements)

References 30 publications

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“…Continuity (Chaves-González et al, 2013) refers to the fact that the same bases are displayed side by side in a confined area. The continuous presence of the same base in a limited region can cause the DNA sequence to stack or distort.…”

Section: Continuitymentioning

confidence: 99%

“…Hairpin structure (Chaves-González et al, 2013) is a secondary structure caused by the stacking of the DNA sequence itself, which may lead to inaccurate calculation. The hairpin structure is composed of a hair ring and hair stem.…”

Section: Hairpin Structurementioning

confidence: 99%

“…Shin et al (2005) carried out multi-objective optimization for DNA sequences, including continuity, similarity, hairpin structure, H-measure, and GC content. Because the traditional algorithm cannot address the heterogeneity and conflict of DNA sequences, scientists designed a multi-objective optimization algorithm based on the artificial bee colony (MO-ABC) (Chaves-González et al, 2013), in which six kinds of conflict problems were solved, and finally, a reliable DNA sequence was generated. In 2014, in order to obtain effective DNA sequences, they proposed to use the multi-objective differential evolution algorithm (DEPT) (Chaves-González and Vega-Rodríguez, 2014) to optimize DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Continuitymentioning

confidence: 99%

Section: Hairpin Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

Wei

Wang

et al. 2021

Front. Genet.

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“…Moreover, they used the NDS archive technique of PAES [47]. Gonzalez et al [49] developed MOABC (multiobjective artificial bee colony) algorithm for the same problem. This algorithm is similar to the original ABC (artificial bee colony) algorithm, but the author has adopted features like a non-dominating sorting technique from NSGA-II [7] and the NDS archive technique of PAES [47].…”

Section: B Optimization Techniques For Sequence Design Of Dna Problemmentioning

confidence: 99%

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

2020

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Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.

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“…Zhu et al [7] proposed an IBPSO algorithm to solve the DNA sequence design problem, as well as further improving the quality of DNA sequences. Chaves-González et al [8] fused artificial bee colony algorithms to propose a new evolutionary approach to create a DNA sequence on the strength of multi-objective swarm intelligence to automatically generate reliable DNA strands that can be applied to molecular computing. Yang et al [9] improved the spatial dispersion in the traditional IWO algorithm and used the IWO algorithm and the niche crowding in the algorithm to solve the DNA sequence design problem.…”

Section: Introductionmentioning

confidence: 99%

Improved Multi-Strategy Matrix Particle Swarm Optimization for DNA Sequence Design

et al. 2023

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The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency.

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A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

Cited by 28 publications

References 30 publications

Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

A Many-Objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design

Improved Multi-Strategy Matrix Particle Swarm Optimization for DNA Sequence Design

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