Novel Adaptive Quantum-inspired Bacterial Foraging Algorithms for Global Optimization

In this paper, novel bacterial foraging algorithms (BFA) based on quantum principle, adaptive chemotactic step size and spiral dynamics algorithm (SDA) are presented. Then, an adaptive chemotactic step size scheme based on iteration number is obtained via analytical approach. Furthermore, the adaptive chemotactic step size based on individual bacterium fitness value and the current iteration number is proposed. The chemotactic step size schemes can be more dynamically varied and hence better exploration and exploitation strategies are introduced. Swarming mechanism is removed from BFA and the role is played by SDA. With the combination of different strategies, two versions of quantum-inspired bacterial foraging algorithm (QBFA) are proposed. The performance of the proposed algorithms is tested with seven basic benchmark functions and seven CEC05 test functions, and compared with original QBFA and two other adaptive bacterial foraging algorithms. Based on the experiment results, two tailed t-test, nonparametric Wilcoxon signed rank test and Friedman test are used to check the significant difference in the performance of the algorithms. The results show that the proposed algorithms outperform the reference algorithms in terms of accuracy and convergence speed.