摘要：Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum-inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–Morse, and Coulomb-like square root potential fields, respectively. To show the computational efficacy of the proposed optimization techniques, the paper presents a comparative study with the classical particle swarm optimization (PSO), genetic algorithm (GA), and firefly algorithm (FFA). The algorithms are used to solve 24 benchmark functions that are categorized by unimodal, multimodal, and fixed-dimension multimodal. As a finding, the algorithm inspired in the Lorentz potential field presents the most balanced computational performance in terms of exploitation (accuracy and precision), exploration (convergence speed and acceleration), and simulation time compared to the algorithms previously mentioned. A deeper analysis reveals that a strong potential field inside a well with weak asymptotic behavior leads to better exploitation and exploration attributes for unimodal, multimodal, and fixed-multimodal functions.
