Fractal-based algorithm: A new metaheuristic method for continuous optimization
Abstract
In this paper a population-based metaheuristic algorithm named fractal-based algorithm is developed to solve continuous optimization problems. In this algorithm, the density of high quality and promising points in an area is considered as a heuristic which estimates the degree of promise of that area for finding the optimal solution. Afterward, the promising areas of state space are iteratively detected and partitioned into self-similar and fractalshaped subspaces for being searched more precisely and more extensively. The proposed algorithm is compared with some metaheuristic algorithms. The results demonstrate that the algorithm is able to find high quality solutions within appropriate time. © 2017 [International Journal of Artificial Intelligence].