Single objective optimization algorithms are the basis of the more complex optimization algorithms such as multi-objective, niching, dynamic, constrained optimization algorithms and so on. Research on single objective optimization algorithms influence the development of the optimization branches mentioned above. In the recent years, various kinds of novel optimization algorithms have been proposed to solve real-parameter optimization problems. This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without knowing the exact equations of the test functions (ie blackbox optimization). We encourage all researchers to test their algorithms on the CEC’15 test suites. The participants are required to send the final results (after submitting their final paper version in March 2015) in the format specified in this technical report to the organizers. The organizers will present an overall analysis and comparison based on these results. We will also use statistical tests on convergence performance to compare algorithms that eventually generate similar final solutions. Papers on novel concepts that help us in understanding problem characteristics are also welcome. Results of 10D and 30D problems are acceptable for the first review submission. However, other dimensional results as specified in the technical report should also be included in the final version, if space permits. Thus, final results for all dimensions in the format introduced in the technical report should be zipped and sent to the organizers after the final version of the paper is submitted.