TSA: Tree-seed algorithm for continuous optimization

This paper presents a new intelligent optimizer based on the relation between trees and their seeds for continuous optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n-dimensional search space corresponds with the possible solution of an optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function optimization and is an alternative optimization method for solving multilevel thresholding problem. (C) 2015 Elsevier Ltd. All rights reserved.