Improved Particle Swarm Based on Elastic Collision for DNA Coding Optimization Design

Donglin Zhu,Changjun Zhou,Zuwei Huang,Linpeng Xie

doi:10.1109/access.2022.3150275

Abstract

In DNA computing, the design of DNA coding sequences is an important factor affecting the reliability of DNA computing. In different DNA sequence designs, suitable constraints should be selected and the sequence design should be rationalized according to these constraints. In this paper, an improved particle swarm optimization algorithm based on elastic collision strategy (EC-PSO) is used to optimize the design of DNA sequences by using an adaptation function that satisfies multiple constraints. EC-PSO uses the idea of elastic collision to improve the optimal and worst positions within the population, introduces the flight means of the sparrow search algorithm (SSA) to enhance the search capability of the algorithm and increase the diversity of the population; then introduces the harmony search algorithm to the population is then fine-tuned to improve the quality of the solution. The effectiveness of the algorithm was verified by comparing it with the other six algorithms in eight test functions. Finally, the sequence designed was more reasonable in the DNA optimal design experiment.

Highlights

With the rapid development of science and technology, the data generated by human beings every day is increasing exponentially, so research and development of highperformance computing is urgent
In order to balance the local search and global search capability of the algorithm, this paper proposes an improved particle swarm algorithm based on elastic collision
The following work has been carried out in this paper: Compared with Sparrow Search Algorithm (SSA) [24], Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO) [25], Teaching learning based optimization (TLBO) [26], Manta Ray Foraging Optimization (MRFO) [27] and whale Optimization Algorithm (WOA)[28] on 8 standard test functions, the results show that EC-PSO has better optimization ability

Summary

INTRODUCTION

With the rapid development of science and technology, the data generated by human beings every day is increasing exponentially, so research and development of highperformance computing is urgent. Ibrahim et al [14] proposed a binary particle swarm optimization algorithm to design DNA coding sequences. Particle swarm optimization (PSO) is a classical metaheuristic algorithm, it has good optimization ability and has few parameters and is easy to realize, but it has some defects such as large randomness and easy local convergence To solve these problems, scholars have made many improvements. The fifth section introduces the comparison and analysis of EC-PSO and other optimization algorithms in DNA coding sequence design. In DNA computing, it is required that the DNA coding sequence should have the same melting temperature as far as possible, so as to better control the reaction between DNA molecules and effectively reduce the probability of non-specific hybridization. R is the gas constant (1.987 cal/kmol), CT is the concentration of DNA

Introduction to EC-PSO algorithm

Elastic Collision strategy

DNA sequence optimization experiment based on EC-PSO

CONCLUSION