CEC 13

Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. ... This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions. We encourage all researchers to test their algorithms on the CEC’13 test suite which includes 28 benchmark functions. The participants are required to send the final results in the form at specified in the technical report to the organizers. The organizers will present an overall analysis and comparison based on these results. We will also use statistical tests on convergence performance to compare algorithms that eventually generate similar final solutions. Papers on novel concepts that help us in understanding problem characteristics are also welcome. The C and Matlab codes for CEC’13 test suite can be downloaded from the website given below: http://www.ntu.edu.sg/home/EPNSugan/index_files/CEC2013/CEC2013.htm

References in zbMATH (referenced in 52 articles )

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  1. Ren, Hao; Li, Jun; Chen, Huiling; Li, ChenYang: Adaptive Lévy-assisted salp swarm algorithm: analysis and optimization case studies (2021)
  2. Chacón Castillo, Joel; Segura, Carlos: Differential evolution with enhanced diversity maintenance (2020)
  3. Chen, Yang; Pi, Dechang: An innovative flower pollination algorithm for continuous optimization problem (2020)
  4. Huang, Jianqiang; Ma, Yan: Bat algorithm based on an integration strategy and Gaussian distribution (2020)
  5. Liang, Jing; Li, Yaxin; Qu, Boyang; Yu, Kunjie; Hu, Yi: Mutation strategy selection based on fitness landscape analysis: a preliminary study (2020)
  6. Mu, Lei; Wang, Peng; Xin, Gang: Quantum-inspired algorithm with fitness landscape approximation in reduced dimensional spaces for numerical function optimization (2020)
  7. Yu, Helong; Zhao, Nannan; Wang, Pengjun; Chen, Huiling; Li, Chengye: Chaos-enhanced synchronized bat optimizer (2020)
  8. Bajer, Dražen; Zorić, Bruno: An effective refined artificial bee colony algorithm for numerical optimisation (2019)
  9. Fister, Iztok; Iglesias, Andres; Galvez, Akemi; Del Ser, Javier; Osaba, Eneko; Perc, Matjaž; Slavinec, Mitja: Novelty search for global optimization (2019)
  10. Luo, Jie; Chen, Huiling; Heidari, Ali Asghar; Xu, Yueting; Zhang, Qian; Li, Chengye: Multi-strategy boosted mutative whale-inspired optimization approaches (2019)
  11. Yang, Zan; Qiu, Haobo; Gao, Liang; Jiang, Chen; Zhang, Jinhao: Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems (2019)
  12. Zhang, Jinghua; Dong, Ze: Parameter combination framework for the differential evolution algorithm (2019)
  13. Zhou, Lingyun; Ding, Lixin; Ma, Maode; Tang, Wan: An accurate partially attracted firefly algorithm (2019)
  14. Abdul Aziz, Nor Hidayati; Ibrahim, Zuwairie; Ab Aziz, Nor Azlina; Mohamad, Mohd Saberi; Watada, Junzo: Single-solution simulated Kalman filter algorithm for global optimisation problems (2018)
  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. Fan, Qinqin; Yan, Xuefeng; Zhang, Yilian: Auto-selection mechanism of differential evolution algorithm variants and its application (2018)
  17. Feng, Xiang; Xu, Hanyu; Yu, Huiqun; Luo, Fei: Particle state change algorithm (2018)
  18. Piotrowski, Adam P.: Across neighborhood search algorithm: a comprehensive analysis (2018)
  19. Tirumala, Sreenivas Sremath: A quantum-inspired evolutionary algorithm using Gaussian distribution-based quantization (2018)
  20. Xu, Shengguan; Chen, Hongquan: Nash game based efficient global optimization for large-scale design problems (2018)

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