Abstract
Eight nature-inspired algorithms are compared with four advanced adaptive differential evolution (DE) variants and the blind random search on two benchmark sets. One of the benchmark sets is CEC 2011 collection of 22 real-world optimization problems, the latter is the suite of 30 artificial functions defined for the competition of the algorithms within CEC 2014. The results of experiments demonstrate the superiority of the adaptive DE variants both on the real-world problems and the artificial CEC 2014 test suite at all the levels of dimension (10, 30, and 50). Some of the nature-inspired algorithms perform even worse than the blind random search. The results entitle to form a recommendation for practitioners: Do not propose a new original algorithm but select among the optimization algorithms supported by thorough research and good ranking in international competitions of optimization algorithms.
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