Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism

Chao Yang,Hai-Yang Wen,Lin Jia,Bing-Qiu Chen

doi:10.1155/2020/2807056

Abstract

The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.

Highlights

As a heuristic algorithm to solve complex problems with a high mutation rate, the clonal selection algorithm has two important characteristics: it has efficient optimization performance [1] and it is difficult to get into local optimum [2]
In terms of Complexity theoretical analysis, Hong et al [16] analyzed the convergence of elitist clonal selection algorithm from the perspective of the state transition probability matrix, Markov chain, and other random process-related theories and proposed a method to evaluate the convergence property of the algorithm, which has a certain reference value. is algorithm is better than the genetic algorithm in terms of automatic node adjustment of B-spline curves
To solve the problem that the CSA cannot in a timely way eliminate antibodies that are not adapted to the new environment and form an antibody black hole, we see that by changing the receptor editing mechanism of the clonal selection algorithm to a new forgetting mechanism, the antibody candidate set can be replaced and updated under the regulation of Rac1 protein

Summary

Introduction

As a heuristic algorithm to solve complex problems with a high mutation rate, the clonal selection algorithm has two important characteristics: it has efficient optimization performance [1] and it is difficult to get into local optimum [2]. As a result, it has attracted the attention of scholars in related fields. Is algorithm is better than the genetic algorithm in terms of automatic node adjustment of B-spline curves It can be seen from the above literature that the current clonal selection algorithm flow mainly includes steps of selection, cloning, mutation, reelection, and replacement. Aiming at the black hole formed by antibodies whose affinity does not meet the mutation and update conditions, the forgetting mechanism is applied to the antibody candidate set update process of the algorithm. e aim is to update these inactive and long-lasting antibodies in the candidate antibody set and enhance the diversity of antibodies in the antibody set, thereby increasing the convergence speed of the algorithm

Overview of the Biological Forgetting Mechanism

Clonal Selection Algorithm and Forgetting Mechanism

Result

Findings

Conclusion