Single-agent Finite Impulse Response Optimizer (SAFIRO) is a recently proposed metaheuristic optimization algorithm which adopted the procedure of the ultimate unbiased finite impulse response filter (UFIR) in state estimation. In SAFIRO, a random mutation with shrinking local neighborhood method is employed during measurement phase to balance the exploration and the exploitation process. Beta, β, is one of the parameters used in the local neighborhood to control the step size. In this study, the effect of β towards the performance of SAFIRO is observed by assigning the value of 1, 5, 10, 15, and 20. The best setting of β for SAFIRO is also determined. The CEC2014 Benchmark Test Suite is used to evaluate the SAFIRO performance with different β values. Results show that the performance of β is depending on the problems to be optimized. 17 out of 30 functions show the best performance of SAFIRO by setting β = 10. Statistical analysis using Friedman test and Holm post hoc test were performed to rank the performance. β = 10 has the highest rank where its performance is significantly better than other values, but equivalent to β = 5 and β = 15. Hence, it is recommended to tune the β for best performance, however, β = 10 is a good value to be used in SAFIRO for solving optimization problems.

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The simulated Kalman filter (SKF) is a population-based optimization algorithm that was developed based on a well-known estimator called Kalman filter. Meanwhile, a white hole operator has been recently introduced to prevent premature convergence in black hole algorithm (BHA). The computation of white hole operator begins by selecting the worst agent as the white hole with event horizon. If an agent is located within the event horizon of white hole, the agent is pushed by the white hole. In this study, the white hole operator is used to improve the effectiveness of the SKF optimizer. A comprehensive experiment is done to evaluate the proposed SKF with white hole operator (SKFWH).