https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

Abstract

This work proposes a new metaheuristic algorithm: a fixed-step average and subtraction-based optimizer (FS-ASBO). This algorithm is the improved version of the average and subtraction-based optimizer (ASBO). There are several improvements related to the original ASBO. First, the proposed algorithm replaces the randomized step size in the guided movement with the fixed step size. Second, the proposed algorithm adds an exploration mechanism after the guided movement in every iteration when the new candidate fails to find a better solution. This proposed algorithm is then implemented into a simulation to evaluate its performance. Through simulation, the proposed algorithm is challenged to solve theoretical optimization problems and real-world optimization problems. The 23 well-known benchmark functions represent the theoretical optimization problem. Meanwhile, the housing optimization problem represents the real-world one. In the simulation, the proposed algorithm is compared with particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), static Komodo algorithm (SKA), and ASBO. The result shows that this proposed algorithm is competitive to solve theoretical problem and superior to solve real-world problem. The proposed algorithm outperforms all sparing algorithms in solving seven functions. In housing optimization problem, it creates 12%, 10%, 8%, 11%, and 10% better total gross profit than the ASBO, PSO, MPA, KMA, and SKA.