An innovative flower pollination algorithm for continuous optimization problem |
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| Institution: | 2. Department of Mathematics, SRM Institute of Science & Technology, Chennai 603 203, India;1. School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China;2. Department of Public Basic Courses, Nanjing Institute of Industry Technology, Nanjing, Jiangsu 210023, China;1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, China;2. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China;3. School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology,Hangzhou, China;1. Key Laboratory of High Performance Manufacturing for Aero Engine (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi''an, China;2. Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI 53706–1572, United States;3. Engineering Research Center of Advanced Manufacturing Technology for Aero Engine, Ministry of Education, Northwestern Polytechnical University, Xi''an, China |
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| Abstract: | The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has received widespread attention and been applied in various engineering fields. However, the FPA still has certain drawbacks, such as inadequate optimization precision and poor convergence. In this paper, an innovative flower pollination algorithm based on cloud mutation is proposed (CMFPA), which adds information of all dimensions in the global optimization stage and uses the designed cloud mutation method to redistribute the population center. To verify the performance of the CMFPA in solving continuous optimization problems, we test twenty-four well-known functions, composition functions of CEC2013 and all benchmark functions of CEC2017. The results demonstrate that the CMFPA has better performance compared with other state-of-the-art algorithms. In addition, the CMFPA is implemented for five constrained optimization problems in practical engineering, and the performance is compared with state-of-the-art algorithms to further prove the effectiveness and efficiency of the CMFPA. |
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