Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

Abstract

Antlion optimization algorithm has good search and development capabilities, but the influence weight of elite ant lions is reduced in the later stage of optimization, which leads to slower algorithm convergence and easy to fall into local optimization. For this purpose, an antlion optimization algorithm based on immune cloning was proposed. In the early stage, the reverse learning strategy was used to initialize the ant population. The Cauchy mutation operator was added to the elite antlion update to improve the later development ability of the algorithm; finally, the antlion was cloned and mutated with the immune clone selection algorithm to change the position and fitness value of the antlion, and further improve the algorithm's global optimization ability and convergence accuracy. 10 test functions and a 0~1 backpack were used to evaluate the optimization ability of the algorithm and applied to the size and layout optimization problems of the truss structure. The optimization effect was found to be good through the force effect diagram. It is verified that ICALO is applied to combinatorial optimization problems with faster convergence speed and higher accuracy. It provides a new method for structural optimization.This article is submitted as original content. The authors declare that they have no competing interests.