Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

Abstract

This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE Congress on Evolutionary Computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm.