# An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO
## 6 Conclusions  
In this paper, we use an improved HHO algorithm to construct a set of constrained codes in DNA storage. We combined a nonlinear convergence strategy with a random opposition-based learning strategy using the original HHO algorithm, which we termed NOL-HHO. We compared this algorithm with HHO, GA, PSO, GWO, and other algorithms based on 23 test functions, and found that NOL-HHO outperformed other optimizers, demonstrating the effectiveness of our improvements. At the same time, the NOL-HHO was compared with the newly proposed optimizer DA and the classical algorithms, GA and PSO, using a Wilcoxon rank-sum test of 19 test functions. The statistical result P-values confirmed that there was a significant gap between NOL-HHO and the population of other optimizers in almost all cases, indicating that it was more competitive. As a new storage medium, [PERSON] et al. described the work related to DNA storage in detail in the paper [55], which shows that DNA storage has strong development prospects for many practical applications. As a new storage medium, DNA storage has strong development prospects for many practical applications. A constrained coding sequence can guarantee good robustness between sequences and prevent decoding errors. At the same time, an optimally constrained code sequence set needs to be constructed to make it possible to store large files in DNA, and to increase the storage capacity and coding rate. We compared the sequence sets constructed by NOL-HHO with those constructed in previous works and demonstrated that NOL-HHO generates a sequence set with a significantly better lower bound. This should promote further development of DNA storage, using our successful improvement of this algorithm. In addition, the constructed reliable DNA coding set can also be applied to other fields of DNA, such as DNA neural network computing model [56], DNA coding image encryption [57], DNA parallel computing model [58; 59], Using the DNA storage architecture to make embedded storage materials [60], etc., which has a wide range of application value. In the future, we plan to further refine the NOL-HHO algorithm. As far as the results of NOL-HHO are concerned, functions such as \(F_{7}\) and \(F_{20}\) were still locally optimal, and there was still room for enhancement. In terms of storage, sequencing remains a major challenge for DNA storage. [PERSON] et al. [61] successfully proposed and demonstrated a decoding method combining DNA assembly with random access, which greatly improved the throughput of nanopore sequencing, promoted the rapid development of DNA storage, and laid a foundation for our next research work. On the one hand, we will continue to raise the lower bound of the restricted code set and strive to store more information than the lower bounds of previous work. On the other hand, we will work on solving the problems in sequencing. The following are available online at [[http://www.mdpi.com/1422-0067/21/6/2191/s1](http://www.mdpi.com/1422-0067/21/6/2191/s1)]([http://www.mdpi.com/1422-0067/21/6/2191/s1](http://www.mdpi.com/1422-0067/21/6/2191/s1)). Conceptualization, Q.Y. ; investigation, B.C. ; resources, X.L. ; writing--original draft preparation, Q.Y. ; writing--review and editing, B.W. ; funding acquisition, Q.Z. ; X.W. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Key R&D Program of China (no. 2018 YFC0910500), the National Natural Science Foundation of China (nos. 61425002, 61751203, 61772100, 61972266, 61802040, 61672121, 61572093), the Program for Changjiang Scholars and Innovative Research Team in University (no. IRT. -15R07), the Program for Liaoning Innovative Research Team in University (no.LIT2017012), the Natural Science Foundation of Liaoning Province (nos. 20180551241, 2019-ZD-0567), the High-level Talent Innovation Support Program of Dalian City(nos.2017 RQ060, 2018 RQ75), the Dalian Outstanding Young Science and Technology Talent Support Program (no.