The bat algorithm (BA) was a kind of meta-heuristic algorithm that was simple and efficient in optimization, it had been widely applied in various fields. To improve the capability of the original BA, two methods used frequently in improvements were proposed in this paper: the gradient and sub-gradient methods, together with the Levy flights. Simulation experiments were carried out on the representatives of unimodal and multimodal benchmark functions. Results confirmed that not all traditional improvements were effective, some of the improved BA even work worse than the original one. However, the Levy flights could be considered a better replacement of randomness in applications.
In the improvement of many meta-heuristic algorithms, two or more algorithms are often mixed into one algorithm to improve the convergence speed and accuracy of the algorithm. The search ability of Harris Hawk optimization (HHO) algorithm [1] is relatively weak, and the search ability of Aquila Optimizer (AO) is relatively strong. So, in this article, the Aquila Optimizer (AO) algorithm is mixed on the basis of the piecewise linear map enabled Harris Hawk optimization (HHO) algorithm, and the advantages of the AO algorithm are used to complement the disadvantages of the piecewise linear map enabled HHO algorithm. This hybrid algorithm is called CHHOAO. The results show that this hybrid improvement is effective and can significantly improve the optimization ability of the algorithm.
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