MOTSA: Multi-objective tree-seed algorithm

The goals identified in real-world problems often consist of concurrently optimization of multiple conflicting goals. At this point, the creation of a multi-objective version of a single-objective optimization method will provide the opportunity to produce solutions to multiple problems concomitantly. Tree Seed Algorithm, TSA for short, has been proposed for the solution of a single-targeted optimization algorithm by inspiring the relationship between trees and seeds in nature. A multiobjective variant tree seed algorithm MOTSA has been proposed to solve multiobjective optimization problems based on the performance of the TSA on single objective problems. The algorithm that proposed has been tested using several criterions optimization algorithms, propagation and convergance success criteria. 9 different confornity functions were used in the tests that performed in Java. In the study, the results obtained in the tests performed according to the specified criteria that are determined. The success that achieve of MOTSA was evaluated. The properties of the algorithm in reaching the optimum result method were evoluated. In order to overcome the problem of selection, as different to single-purpose for multi-objective problems, NSGA-II's well-known strategies, non-dominant sorting and crowd distance features are integrated with the proposed MOTSA. By doing this, the highest quality solutions are selected from the combined tree and seed populations in the MOTSA algorithm and passed on to the next generation. By synthesizing the best aspects of TSA and NSGA-II algorithms, MOTSA algorithm is used to solve multi-objective optimization problems.