An intelligent metaphor-free spatial information sampling algorithm for balancing exploitation and exploration

In this paper, we propose an intelligent scheme and design a spatial information sampling algorithm (SIS) to achieve balance between exploitation exploration more efficiently. SIS, by creatively using chaotic map, divide the population of into external internal parts activate corresponding or operations according location where optimal individuals appear in population. The makes full use solution space algorithm’s is located. It also gives authority start autonomously based on space, which increases flexibility complex optimization problems. This novel not only further promotes intelligence simple architecture but creates new way balancing process. We test SIS IEEE CEC2017, CEC2011, artificial neural model training problem, position problem wave energy converters. When compared other state-of-the-art meta-heuristics, results Wilcoxon signed-rank test, rank-sum Friedman revealed that possesses superior solutions terms global optimality, avoidance local minima, quality. source code released at https://toyamaailab.github.io/.