Locally and Globally Tuned Chaotic Biogeography-Based Optimization Algorithm

In this paper, we have used chaotic maps to improve locally and globally tuned biogeography-based optimization (LGBBO) algorithm. The effect of chaotic maps like Chebyshev, Logistic, Sinusoidal, and Circle for enhancing the efficiency of LGBBO are studied in terms of local optima avoidance and convergence speed, and we name it as Locally and Globally Tuned Chaotic BBO(LGCBBO). We have carried out an extensive numerical evaluation on ten high dimensional benchmark functions to measure the efficiency of our proposed method. The experimental study confirms that LGCBBO is better than LGBBO for some chaotic maps in terms of accuracy and convergence rate.