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 176 articles )

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  1. Chen, Guodong; Li, Yong; Zhang, Kai; Xue, Xiaoming; Wang, Jian; Luo, Qin; Yao, Chuanjin; Yao, Jun: Efficient hierarchical surrogate-assisted differential evolution for high-dimensional expensive optimization (2021)
  2. D’Angelo, Gianni; Palmieri, Francesco: GGA: a modified genetic algorithm with gradient-based local search for solving constrained optimization problems (2021)
  3. Salgotra, Rohit; Singh, Urvinder; Singh, Gurdeep; Mittal, Nitin; Gandomi, Amir H.: A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems (2021)
  4. Salgotra, Rohit; Singh, Urvinder; Singh, Supreet; Singh, Gurdeep; Mittal, Nitin: Self-adaptive salp swarm algorithm for engineering optimization problems (2021)
  5. Chacón Castillo, Joel; Segura, Carlos: Differential evolution with enhanced diversity maintenance (2020)
  6. Liang, Jing; Li, Yaxin; Qu, Boyang; Yu, Kunjie; Hu, Yi: Mutation strategy selection based on fitness landscape analysis: a preliminary study (2020)
  7. Li, Wei; Meng, Xiang; Huang, Ying; Fu, Zhang-Hua: Multipopulation cooperative particle swarm optimization with a mixed mutation strategy (2020)
  8. Wang, Chun-feng; Liu, Kui; Shen, Pei-ping: A novel genetic algorithm for global optimization (2020)
  9. Wang, Shengliang; Liu, Genyou; Gao, Ming; Cao, Shilong; Guo, Aizhi; Wang, Jiachen: Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (2020)
  10. Zhu, Shun-Peng; Keshtegar, Behrooz; Bagheri, Mansour; Hao, Peng; Trung, Nguyen-Thoi: Novel hybrid robust method for uncertain reliability analysis using finite conjugate map (2020)
  11. Ziadi, Raouf; Bencherif-Madani, Abdelatif; Ellaia, Rachid: A deterministic method for continuous global optimization using a dense curve (2020)
  12. Babayan, Narek; Tahani, Mojtaba: Team arrangement heuristic algorithm (TAHA): theory and application (2019)
  13. Harrison, Kyle Robert; Ombuki-Berman, Beatrice M.; Engelbrecht, Andries P.: A parameter-free particle swarm optimization algorithm using performance classifiers (2019)
  14. Luo, Jie; Chen, Huiling; Heidari, Ali Asghar; Xu, Yueting; Zhang, Qian; Li, Chengye: Multi-strategy boosted mutative whale-inspired optimization approaches (2019)
  15. Chen, Xu; Xu, Bin; Yu, Kunjie; Du, Wenli: Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering (2018)
  16. Elkhechafi, Mariam; Hachimi, Hanaa; Elkettani, Youssfi: A new hybrid Cuckoo search and firefly optimization (2018)
  17. Fan, Qinqin; Yan, Xuefeng; Zhang, Yilian: Auto-selection mechanism of differential evolution algorithm variants and its application (2018)
  18. Long, Wen; Jiao, Jianjun; Liang, Ximing; Tang, Mingzhu: Inspired grey wolf optimizer for solving large-scale function optimization problems (2018)
  19. Xu, Shengguan; Chen, Hongquan: Nash game based efficient global optimization for large-scale design problems (2018)
  20. Zhang, Ming; Tian, Na; Palade, Vasile; Ji, Zhicheng; Wang, Yan: Cellular artificial bee colony algorithm with Gaussian distribution (2018)

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