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.