Abstract

Meta-heuristics utilizing numerous parameters are more complicated than meta-heuristics with a couple of parameters for various reasons. In essence, the effort expected to tune the strategy-particular parameters is far more prominent as the quantity of parameters increases and furthermore, complex algorithms are liable for the presence of further parameter interactions. Jaya meta-heuristic does not involve any strategy-specific parameters and is a one-stage technique. It has demonstrated its effectiveness compared to major types of meta-heuristics and it introduces various points of interest, such as its easy deployment and set-up in industrial applications and its low complexity to be studied. In this work, a new meta-heuristic, Enhanced Jaya (EJaya) is proposed to overcome the inconsistency of Jaya in diverse situations, introducing coherent attraction and repulsion movements and restrained intensity for flight. Comparative results of EJaya in a set of benchmark problems including statistical tests show that it is feasible to increase the accuracy, scalability and exploitation capability of Jaya while keeping its specific parameter-free feature. EJaya is especially suitable for a priori undefined characteristics optimization functions or applications where the set-up time of the optimization process is critical and parameters tuning and interactions must be avoided.

Keywords

MSC

90-08

Cite this article

@article{cai-3893,
  author  = {Rafael Rodríguez-Reche and Rocío P. Prado and Sebastián García-Galán and José Enrique Muñoz-Expósito and Nicolás Ruiz-Reyes},
  title   = {Specific Parameter-Free Global Optimization to Speed Up Setting and Avoid Factors Interactions},
  journal = {Computing and Informatics},
  volume  = {38},
  number  = {2},
  pages   = {265--290},
  year    = {2019},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2019_2_265}
}
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