MOTSA: A Multi-Objective Tree-Seed Algorithm
Gül Özcan Ahmet Özkış Mustafa Servet Kıran
AbstractThe Tree-Seed Algorithm, TSA for short, has been proposed for solving single-objective optimization algorithm by inspiring the relation trees and seeds in nature. In this study, its multi-objective variant tree-seed algorithm, MOTSA, is proposed for solving multi-purpose optimization problems by motivating its performance on single-objective problems. In order to overcome selection issue in multi-objective problems, the well-known strategies, non-dominated sorting and crowding distance, of NSGA-II has been integrated with the proposed MOTSA. By doing this, the highest quality solutions from the combined populations of trees and seeds are selected and transferred to the next generation. The MOTSA are applied to solve well-known three multi-objective benchmark problems and obtained results show that MOTSA is an alternative multi-objective optimization algorithm.