Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization

Research on the single objective optimization algorithms is the basis of the research on the more complex optimization algorithms such as multi-objective optimizations algorithms, niching algorithms, constrained optimization algorithms and so on. All new evolutionary and swarm algorithms are tested on single objective benchmark problems. In addition, these single objective benchmark problems can be transformed into dynamic, niching composition, computationally expensive and many other classes of problems. In the recent years various kinds of novel optimization algorithms have been proposed to solve real-parameter optimization problems, including the CEC'05 and CEC'13 Special Session on Real-Parameter Optimization [1][2]. Considering the comments on the CEC'13 test suite, we organize a new competition on real parameter single objective optimization. For this competition, we are developing benchmark problems with several novel features such as novel basic problems, composing test problems by extracting features dimension-wise from several problems, graded level of linkages, rotated trap problems, and so on. This competition excludes usage of surrogates or meta-models. There is a sub-competition to test the algorithms with a very small number of function evaluations in order emulate the computationally expensive optimization scenario. This sub-competition encourages the usage of surrogates and other approximation approaches. This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions. We encourage all researchers to test their algorithms on the CEC'14 test suite which includes 30 benchmark functions. The participants are required to send the final results in the format specified in the 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 generate similar final solutions eventually. Papers on novel concepts that help us in understanding problem characteristics are also welcome.