EDOA: An Elastic Deformation Optimization Algorithm

In recent years, a large number of meta-heuristic algorithms have been proposed to efficiently solve various complex optimization problems in reality. Most of these algorithms are based on the intelligent behavior of swarms in the natural world. In this article, we take Hooke's law of elasticity and Newton's second law of motion as the information interaction tools and innovatively propose a new meta-heuristic algorithm that is based on the laws of physics, called the elastic deformation optimization algorithm (EDOA). A new parameter adaptive adjustment mechanism is designed in the EDOA to better explore and exploit the search space. At the same time, we compare the proposed EDOA with six well-known search algorithms and conduct simulation experiments on 23 classical benchmark functions and IEEE CEC 2020 benchmark functions respectively. We have further analyzed the experimental results, used two nonparametric statistical test methods, and drawn iterative curves of the algorithms to prove the powerful comprehensive performance of the proposed EDOA.

Automated summary

In this article, a new meta-heuristic algorithm based on the laws of physics, called the elastic deformation optimization algorithm (EDOA), is proposed. The EDOA utilizes Hooke's law of elasticity and Newton's second law of motion as information interaction tools. It also incorporates a parameter adaptive adjustment mechanism to better explore and exploit the search space. Through simulation experiments and statistical analysis, the powerful comprehensive performance of the EDOA is demonstrated.