In a search process, getting trapped in a local minimum or jumping the global minimum problems are also one of the biggest problems of meta-heuristic algorithms as in artificial intelligence methods. In this paper, causes of these problems are investigated and novel solution methods are developed. For this purpose, a novel framework has been developed to test and analyze the meta-heuristic algorithms. Additionally, analysis and test studies have been carried out for Symbiotic Organisms Search (SOS) Algorithm. The aim of the study is to measure the mimicking a natural ecosystem success of symbiotic operators. Thus, problems in the search process have been discovered and operators' design mistakes have been revealed as a case study of the developed testing and analyzing method. Moreover, ways of realizing a precise neighborhood search (intensification) and getting rid of the local minimum (increasing diversification) have been explored. Important information that enhances the performance of operators in the search process has been achieved through experimental studies. Additionally, it is expected that the new experimental test methods developed and presented in this paper contributes to meta-heuristic algorithms studies for designing and testing.