A COMPARISON BETWEEN QUANTUM INSPIRED BACTERIAL FORAGING ALGORITHM AND GA-LIKE ALGORITHM FOR GLOBAL OPTIMIZATION

摘要

Bacterial foraging algorithm (BFA) is a population-based stochastic search technique for solving various scientific and engineering problems. However, it is inefficient in some practical situations. In order to improve the performance of the BFA, we propose a novel optimization algorithm, named quantum inspired bacterial foraging algorithm (QBFA), which applies several quantum computing principles, and a new mechanism is proposed to encode and observe the population. The algorithm has been evaluated on the standard high-dimensional benchmark functions in comparison with GA, PSO, GSO and FBSA, respectively. The proposed algorithm is then used to tune a PID controller of an automatic voltage regulator (AVR) system. Simulation results clearly illustrate that the proposed approach is very efficient and could be easily extended to 300 or higher-dimensional problems.