Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

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A potential ecological risk assessment was conducted based on the analyzing results of the typical pollutants (Pb, Cu, Cr, Cd, and Zn) contents in the sediments of Yangtze River within the Wanzhou section using the index number techniques of single factor and Hakanson method for the quality status of the sediments. The results indicted that cadmium (Cd) had the largest pollution index and was the main pollution factor among the metals. The ecological risk sequence of the metals was Cd > Zn >Pb > Cu >Cr, while the sequence of the potential ecological risk posed by the metals was Cd > Pb > Cu> Zn >Cr. The index range of potential ecological risk was from 101.39 to 184.31, and the average index of potential ecological risk factors (RI) was 152.35. The Yangtze River within the Wanzhou section has a middle potential ecological risk.

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