Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems

Abstract

With the progress of science and technology, optimization problems have become complex. Meta-heuristic algorithms have the advantages of high efficiency and strong global search ability in solving optimization problems, so more and more meta-heuristic algorithms are proposed and investigated deeply, and how to improve the universality of an algorithm is an important issue. In this paper, propose an optimization algorithm that simulates human memory behaviour, fully known as human memory optimization Algorithm, abbreviated as HMO, which simulates the way humans behave in production, stores human preferences for success and failure, simulates the way humans behave in their memory, gradually moving towards better directions and outcomes to find a reasonable optimal solution. The results were compared with other meta-heuristic algorithms in the CEC 2013 test set and showed that HMO has better optimization capabilities, and the feasibility of the algorithm was verified from convergence analysis and parametric analysis experiments. In three engineering optimization problems, HMO was able to find optimal solutions within a reasonable range of parameters, verifying the practicality of HMO.