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Chaos Rafflesia Optimization Algorithm

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Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 341))

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Abstract

Intelligent optimization algorithms are one of the crucial methods to solve optimization problems at present. When faced with practical issues in multi-dimensional space, intelligent optimization algorithms can be used to find optimal parameters in the global area to solve the problem. This paper improves the problem that the Rafflesia Optimization Algorithm (ROA) recently proposed is slow and easy to fall into a local optimum. This paper studies the performance of chaotic mapping to optimize the ROA algorithm and uses the random walk strategy to improve the ROA algorithm’s performance and the algorithm’s ability to jump out of the local optimum. This paper tests the improved algorithm (CROA) through the CEC2013 data test set and compares it with other optimization algorithms. The experimental results show that the newly proposed ROA algorithm is more effective.

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Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Xiao Wang & Shu-Chuan Chu

  2. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Lingping Kong

  3. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China

    Dongqiang Yang

Authors
  1. Xiao Wang
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  2. Shu-Chuan Chu
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  3. Lingping Kong
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  4. Dongqiang Yang
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Corresponding author

Correspondence to Shu-Chuan Chu .

Editor information

Editors and Affiliations

  1. Fujian University of Technology, Fuzhou, China

    Shaowei Weng

  2. Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

    Chin-Shiuh Shieh

  3. Department of Informatics, University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wang, X., Chu, SC., Kong, L., Yang, D. (2023). Chaos Rafflesia Optimization Algorithm. In: Weng, S., Shieh, CS., Tsihrintzis, G.A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIHMSP 2022. Smart Innovation, Systems and Technologies, vol 341. Springer, Singapore. https://doi.org/10.1007/978-981-99-0605-5_4

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