New generation of metaheuristics by inspiration from ancient

Abstract

Recently, the development of new metaheuristic algorithms has become very expansive. This expansion is especially evident in the category of nature-inspired algorithms. Nature is indeed the source of the solution in many problems, but the developed algorithms in this category used almost the same procedure for optimization. Before the development of nature-inspired algorithms, evolutionary-based algorithms were introduced. It seems that there is a need for some kind of change in this area. This change can be found in the new generation of algorithm development inspired by the ancient era. Ancient inspiration brings together all the positive aspects of nature and evolution. This paper discusses some applications of the ancient-inspired Giza Pyramids Construction (GPC) algorithm compared to the nature-inspired Emperor Penguins Colony (EPC) algorithm. Applications discussed in this paper include improving k-means clustering and optimizing the neuro-fuzzy system. Results from experiments show that the ancient-inspired GPC algorithm performed superior and more efficiently than algorithms inspired by other sources of inspiration.