Selection methods have an important role in the meta-heuristic search (MHS) process. However, apart from a few successful methods developed in the past, new and effective studies have not been found in recent years. It is known that solution candidates selecting from the population during the search process directly affects the direction and success of the search. In this study, a new selection method based on fitness-distance balance (FDB) is developed in order to solve the premature convergence problem in the MHS process. Thanks to the developed method, solution candidates with the highest potential to improve the search process can be determined effectively and consistently from the population. Experimental studies have been conducted to test and verify the developed FDB selection method. For this purpose, 90 benchmark functions with different types and complexity levels have been used. In order to test the developed FDB method, numerous variants have been formed. These variants have been compared to each other to determine the most effective FDB variant. In the validation study, the FDB-SOS (FDB-based symbiotic organism search) algorithm is compared with thirteen well-known and up-to-date MHS techniques. The search performance of the algorithms has been analyzed by the Wilcoxon Rank Sum Test. The results show that the developed selection method makes a significant contribution to the meta-heuristic search process. (C) 2019 Elsevier B.V. All rights reserved.