A Performance of AFIRO among Asynchronous Iteration Strategy Metaheuristic Algorithms
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Abstract
Asynchronous Finite Impulse Response Optimizer (AFIRO) is a metaheuristic algorithm that has been developed as a population-based solution with an asynchronous update mechanism. AFIRO is inspired by the Ultimate Unbiased Finite Impulse Response filter framework. AFIRO works with a group of agents where each agent performs the iteration update asynchronously. In the original paper, AFIRO was compared with the Particle Swarm Optimisation algorithm, Genetic Algorithm, and Grey Wolf Optimizer. Although AFIRO shows a better performance, the comparison seems unfair since the iteration strategy of AFIRO is different from those compared algorithms. Hence, this article further investigates the potential of AFIRO against three existent metaheuristic algorithms with the same iteration strategy, namely Asynchronous PSO (A-PSO), Asynchronous Gravitational Search Algorithm (A-GSA), and Asynchronous Simulated Kalman Filter (A-SKF). The CEC2014 test suite was applied to evaluate the performance, where the results revealed that AFIRO leads 18 out of 30 functions. The Holm post hoc showed that AFIRO performs significantly better than A-SKF and A-GSA while having the same performance as A- PSO. Moreover, the Friedman test disclosed that AFIRO has the highest ranking than A-PSO, A-SKF, and A-GSA. Therefore, it can be concluded that AFIRO performs well in the same iteration strategy category.
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