In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, an improved BBO algorithm, that is, worst opposition learning and random-scaled differential mutation BBO (WRBBO), is presented in this paper. First, BBO's mutation operator is deleted to reduce the computational complexity and a more efficient random-scaled differential mutation operator is merged into BBO's migration operator to obtain global search ability. Second, in order to balance exploration and exploitation, the BBO's migration operator is replaced with a dynamic heuristic crossover to enhance the local search ability. Finally, a worst opposition learning is merged into the improved algorithm to avoid trapping into local optima. A large number of experiments are made on 18 various kinds of classic benchmark functions and some complex functions from the CEC-2013 test set. In addition, WRBBO is applied to clustering optimization and medical image segmentation. The experimental results show that WRBBO has better optimization efficiency on benchmark function optimization, clustering optimization, and medical image segmentation than quite a few state-of-the-art BBO variants and other algorithms.

In order to obtain a Coyote optimization algorithm (COA) is with universal applicability, this paper proposes a novel hybrid algorithm based on COA and Deferential Evolution (DE), named DECOA. Firstly, Global optimum guidance growth scenario of alpha coyotes is introduced into the growth procedure of the alpha coyote of each group, which enables the scenario to approach the global optimum result faster. Secondly, a Gaussian global growth operator is randomly adopted in the growth procedure of ordinary coyotes to make better the global search capacity while preserving the vigorous local search capacity. Thirdly, in order to further improve the social adaptability of coyotes after they grow up, differential expulsion and admission strategy is integrated. Finally, the numbers of the groups are dynamically modified to balance exploitation and exploration. A large number of tests on the benchmark functions from CEC-2017 and CEC-2013 test sets verify the proposed strategy. Especially, compared to many state-of-the-art algorithms, the proposed strategy ranks first 15 times among 29 benchmark functions of CEC-2017 and the average running speed has reached 2.29s. It is extremely important to obtain the PV cell parameters effectively, because the required data to model PV cells will be not provided by the manufacturers. Hence, DECOA is further applied to the scene of parameter estimation of photovoltaic solar cells and modules and the results still indicate that it surpasses the other advanced comparison scenarios.