DNA Design Based on Improved Ant Colony Optimization Algorithm With Bloch Sphere

Abstract

DNA computing and coding have good prospects in data storage, data computing, data encryption and other fields. At the same time, it is very important to design a set of DNA coding set that meets a variety of constraints in today's research. DNA code aims to find as many qualified DNA data sets as possible under the same constraints. In this paper, we design an ant colony algorithm based on Bloch Sphere. In this algorithm, Bloch system is used to initialize the DNA coding population, and ant colony algorithm is used to find the best DNA coding. Meanwhile, crossover and mutation operations are added to make the generated population more random. Experimental results show that the coding set found by the algorithm is better than other coding sets.

An optimized resource scheduling algorithm based on GA and ACO algorithm

“…However, the performance and parameters of ACO algorithm still need to be optimized. Zhou et al [41] used the bloch system to initialize the DNA coding population, and used the ACO algorithm to find the best DNA coding. Meanwhile, the crossover and mutation operators are added to make the generated population more random and diversified.…”

Complex Branch Pipe Layout Optimization Based on Machine Learning and Improved Grey Wolf Algorithm

“…At present, the research results of pipelaying algorithms mainly focus on single-pipe laying algorithms, and the typical ones are maze algorithm [2], escape algorithms, and intelligent optimisation based pipelaying algorithms. Among the commonly used intelligent optimisation algorithms are simulated annealing algorithm, genetic algorithm, ant colony optimisation algorithm [3], particle swarm algorithm [4], glowworm swarm optimisation algorithm [5] etc. Intelligent optimization algorithms, as an emerging optimization technique with good robustness, have been successfully applied to many optimization problems.…”