A Micro-Differential Evolution Algorithm for Continuous Complex Functions

• Published in: IEEE access Vol. 7; pp. 172783 - 172795
• Main Authors: Olguin Carbajal, M., Herrera-Lozada, J. C., Sandoval-Gutierrez, J., Vasquez-Gomez, J. I., Serrano-Talamantes, J. F., Chavez-Estrada, F. A., Rivera-Zarate, I., Hernandez-Bolanos, M.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Benchmark testing; Continuity (mathematics); Evolutionary algorithms; Evolutionary computation; HappyCat; HGBat; Highly difficult problems; hybrid algorithms; micro-algorithms; Optimization; Proposals; RayES; Searching; Sociology; Statistics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2954296

In this paper, we incorporate a local search procedure into a micro differential evolution algorithm MED with the aim of solving the HappyCat function. Our purpose is to find out if our proposal is more competitive than a Ray-ES algorithm. We test our micro Differential Evolution algorithm (μDE) on HappyCat and HGBat functions. The results that we obtained with micro-DE are better compared with the results the original RayES reference algorithm. This analysis supports our conjecture that a reduced population DE hybridized with a local search (Ray search) is a key combination in dealing with this function. Our results support the hypothesis that a well-focused micro population is more accurate and efficient than existing techniques, representing (that of micro-algorithms) a serious competitor because of its efficiency and accuracy. In fact, the proposed (but never solved) HGBat function can be dealt with, showing the scalability and potential future uses of our technique.