摘要：This paper proposes a new meta-heuristic-based optimization algorithm for single-objective problems. The algorithm is called Natural Reforestation Optimization (NRO) and is inspired by the process in which natural reforestation takes place. The features of this algorithm (such as the distribution of the initial population, the exploration and exploitation mechanisms, the interactions between the particles, the stopping criteria, among others) are discussed and analyzed to show how they are applied to enhance the search of the global solution. The performance of this algorithm is tested with standard single-objective optimization problems (which contain from 2 to 20 optimization variables) and is compared with other optimization algorithms. The results reveal that in general, the NRO algorithm produces solutions close to the global optimal and is able to surpass the other optimization algorithms for many of the benchmark functions. The current study shows the qualities of the NRO algorithm and serves as the starting point for further investigation to take place to keep improving its capabilities.
