A decremental stochastic fractal differential evolution for global numerical optimization

The aim of hybridization in the context of evolutionary computation is to combine appropriate operators from different evolutionary computation paradigms to form a single technique that enjoys a statistically superior performance over a wide range of optimization problems. This paper introduces a novel hybridization between differential evolution and update processes of the stochastic fractal search algorithm. The diffusion property of the fractal search algorithm is applied in random fractals followed by two novel update processes to explore the search space more efficiently. In this algorithm, a diffusion process based on differential evolution algorithm is used instead of random fractals in the original stochastic fractal search algorithm. A new success-based scheme is used to utilize the update processes and to solve the burden of extra computations during the search. This new algorithm captures the strengths of both component algorithms and produces a greater explorative power as compared to the original algorithms. To verify the performance of our algorithm, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The results affirm the effectiveness and robustness of the proposed approach compared to the original stochastic fractal search and other recent state-of-the-art algorithms. The proposed algorithm enjoys a statistically superior performance over most of the tested benchmarks, especially hybrid and composition test functions compared to the other contestant algorithms.