An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

Mathematical Biosciences and Engineering

Rong Zheng¹  Heming Jia¹  Shuang Wang¹  Laith Abualigah²  Qingxin Liu³ 

[1] 1. School of Information Engineering, Sanming University, Sanming 365004, China;2. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan 3. School of Computer Science, Universiti Sains Malaysia, Penang 11800, Malaysia;4. School of Computer Science and Technology, Hainan University, Haikou 570228, China;

关键词: arithmetic optimization algorithm;    meta-heuristic algorithm;    global optimization;    exploration and exploitation;    high-dimensional optimization problems;   

DOI  :  10.3934/mbe.2022023

来源: DOAJ

【 摘 要 】

Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (RMOP) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.

【 授权许可】

Unknown