Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization

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A novel Random Walk Grey Wolf Optimizer (2019, methodology): “As per the guidelines provided by IEEE CEC 2014 [28], for each of the test function 51 runs are performed for the dimensions 10 and 30 to observe the performance of both the algorithms.”
A self-adaptive artificial bee colony algorithm based on global best for global optimization (2017, background): “A detailed description of these test functions can be found in Suganthan et al. (2005).”
Genetic Learning Particle Swarm Optimization (2016, methodology): “To thoroughly evaluate the performance of the proposed GL-PSO, two popular test suites consisting of 42 benchmark functions in total [44], [45] are tested in the numerical experiments.”
Success-history based parameter adaptation for Differential Evolution (2013, methodology): “We performed our evaluation following the guidelines of the CEC2013 benchmark competition [19].”