It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
The recently proposed swarm intelligence Artificial Rabbits Optimization (ARO) performs well, but there are still some drawbacks, including low population diversity, unbalanced exploration and exploitation capabilities, and low convergence accuracy. To address the above issues, this article proposes a variant of ARO named MARO, which adopts three strategies to overcome the limitations of ARO and improve its performance. This paper uses 23 classic test functions and CEC2017 test functions for testing. The experimental results show that MARO has higher convergence speed, accuracy, and stability than the comparison algorithms. In addition, the enormous potential of MARO in practical applications is further verified through five real-world engineering application problems.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Harbin University of Science and Technology, China