Accelerated Opposition Learning based Single Candidate Optimization Algorithm
Citation
Doğan, C., & Yüzgeç, U. (2023). Accelerated opposition learning based single candidate optimization algorithm, 11th International Congress of Academic Studies (ICAR23), 506–514.Abstract
In order to address optimization problems effectively, the development of efficient optimization
algorithms holds paramount importance. This study focuses on developing the search capability of
the Single Candidate Optimization (SCO) algorithm, introduced by Shami et al. in 2022.
Distinguishing itself from other population-based heuristic algorithms, the SCO algorithm aims to
expedite the solution-finding process by employing a single candidate solution. In this study, the
improvement of the SCO algorithm incorporates an accelerated opposition-based learning mechanism
(AccOppSCO). To assess the performance of the proposed AccOppSCO algorithm, it was tested for
various optimization problems from the literature. The evaluation revealed that the AccOppSCO
algorithm can generate more accurate solutions compared to the original SCO algorithm.