Single-agent finite impulse response optimizer for numerical optimization problems - Tasiransurini, Ab Rahman; Zuwairie, Ibrahim; Nor Azlina, Ab. Aziz; Zhao, Shunyi; Nor Hidayati, Abdul Aziz (IEEE, 2018)
This paper introduces a new single-agent metaheuristic optimization algorithm, named single-agent ﬁnite impulse response optimizer (SAFIRO). This proposed algorithm is inspired by the estimation ability of the ultimate iterative unbiased ﬁnite impulse response (UFIR) ﬁlter. The UFIR ﬁlter is one of the variants of the ﬁnite impulse response (FIR) ﬁlter, whereby in state space models, the FIR ﬁlter can be used as an option other than the Kalman ﬁlter (KF) for state estimation. Unlike the KF, the UFIR ﬁlter does not require any noise covariance, error covariance, and initial condition to calculate the state estimate. The UFIR ﬁlter also provides an iterative Kalman-like form to improve the estimation process. In the SAFIRO algorithm, the agent works as an individual UFIR to ﬁnd an optimal or a near-optimal solution, where the agent needs to perform two main tasks; measurement and estimation. The performance of the SAFIRO algorithm is evaluated using the CEC 2014 Benchmark Test Suite for single-objective optimization and statistically compared with the several well-known metaheuristic optimization algorithms, such as Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimization algorithm. The experimental results show that the proposed SAFIRO algorithm is able to converge to the optimal and the near-optimal solutions, and signiﬁcantly outperform all the aforementioned state-of-the-art metaheuristic algorithms.