CEC 05

Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. .. In this report, 25 benchmark functions are given and experiments are conducted on some real-parameter optimization algorithms. The codes in Matlab, C and Java for them could be found at http://www.ntu.edu.sg/home/EPNSugan/. The mathematical formulas and properties of these functions are described in Section 2. In Section 3, the evaluation criteria are given. Some notes are given in Section 4


References in zbMATH (referenced in 166 articles )

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  1. Chacón Castillo, Joel; Segura, Carlos: Differential evolution with enhanced diversity maintenance (2020)
  2. Liang, Jing; Li, Yaxin; Qu, Boyang; Yu, Kunjie; Hu, Yi: Mutation strategy selection based on fitness landscape analysis: a preliminary study (2020)
  3. Wang, Chun-feng; Liu, Kui; Shen, Pei-ping: A novel genetic algorithm for global optimization (2020)
  4. Luo, Jie; Chen, Huiling; Heidari, Ali Asghar; Xu, Yueting; Zhang, Qian; Li, Chengye: Multi-strategy boosted mutative whale-inspired optimization approaches (2019)
  5. Chen, Xu; Xu, Bin; Yu, Kunjie; Du, Wenli: Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering (2018)
  6. Elkhechafi, Mariam; Hachimi, Hanaa; Elkettani, Youssfi: A new hybrid Cuckoo search and firefly optimization (2018)
  7. Fan, Qinqin; Yan, Xuefeng; Zhang, Yilian: Auto-selection mechanism of differential evolution algorithm variants and its application (2018)
  8. Long, Wen; Jiao, Jianjun; Liang, Ximing; Tang, Mingzhu: Inspired grey wolf optimizer for solving large-scale function optimization problems (2018)
  9. Xu, Shengguan; Chen, Hongquan: Nash game based efficient global optimization for large-scale design problems (2018)
  10. Zhang, Ming; Tian, Na; Palade, Vasile; Ji, Zhicheng; Wang, Yan: Cellular artificial bee colony algorithm with Gaussian distribution (2018)
  11. Biswas, Anupam; Biswas, Bhaskar: Analyzing evolutionary optimization and community detection algorithms using regression line dominance (2017)
  12. Cao, Zijian; Wang, Lei: An optimization algorithm inspired by the phase transition phenomenon for global optimization problems with continuous variables (2017)
  13. Rakhshani, Hojjat; Rahati, Amin: Intelligent multiple search strategy cuckoo algorithm for numerical and engineering optimization problems (2017)
  14. Vinkó, Tamás; Gelle, Kitti: Basin hopping networks of continuous global optimization problems (2017)
  15. Wang, Danping; Hu, Kunyuan; Ma, Lianbo; He, Maowei; Chen, Hanning: Multispecies coevolution particle swarm optimization based on previous search history (2017)
  16. Wang, Ling; An, Lu; Pi, Jiaxing; Fei, Minrui; Pardalos, Panos M.: A diverse human learning optimization algorithm (2017)
  17. Zhao, Fuqing; Shao, Zhongshi; Wang, Junbiao; Zhang, Chuck: A hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution (2017)
  18. Cabassi, Federico; Locatelli, Marco: Computational investigation of simple memetic approaches for continuous global optimization (2016)
  19. Cui, Laizhong; Li, Genghui; Lin, Qiuzhen; Chen, Jianyong; Lu, Nan: Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations (2016)
  20. El-Shorbagy, Mohammed A.; Mousa, A. A.; Nasr, S. M.: A chaos-based evolutionary algorithm for general nonlinear programming problems (2016)

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