Multi-swarm Particle Swarm Optimizer with Cauchy Mutation for Dynamic Optimization Problems

Cheng

IEEE Trans. Evol. Computat.

et al. 2021

Section: Diversity Controlmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part A

Cheng

IEEE Trans. Evol. Computat.

et al. 2021

“…To use these performance indicators, the optimum position must be known. D O can be calculated as the average distance between the closest found position to the global optimum over all fitness evaluations, iterations, or at the end of each environment [129]. D O that measures the tracking efficiency of DOAs over all fitness evaluations is formulated as:…”

Section: A Performance Indicatorsmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

Cheng

IEEE Trans. Evol. Computat.

et al. 2021

“…The Cauchy mutation operator is an effective strategy to improve the algorithm (Wang et al, 2007;Hu et al, 2009;Ali and Pant, 2011;Sapre and Mini, 2019). The theoretical basis of the Cauchy mutation operator is derived from the standard Cauchy distribution density function, which is defined by eq.…”

Section: Cauchy Mutation Strategymentioning

confidence: 99%

CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage

Zheng

Wang

2021

Front. Genet.

In the era of big data, new storage media are urgently needed because the storage capacity for global data cannot meet the exponential growth of information. Deoxyribonucleic acid (DNA) storage, where primer and address sequences play a crucial role, is one of the most promising storage media because of its high density, large capacity and durability. In this study, we describe an enhanced gradient-based optimizer that includes the Cauchy and Levy mutation strategy (CLGBO) to construct DNA coding sets, which are used as primer and address libraries. Our experimental results show that the lower bounds of DNA storage coding sets obtained using the CLGBO algorithm are increased by 4.3–13.5% compared with previous work. The non-adjacent subsequence constraint was introduced to reduce the error rate in the storage process. This helps to resolve the problem that arises when consecutive repetitive subsequences in the sequence cause errors in DNA storage. We made use of the CLGBO algorithm and the non-adjacent subsequence constraint to construct larger and more highly robust coding sets.