JADE

JADE: adaptive differential evolution with optional external archive. A new differential evolution (DE) algorithm, JADE, is proposed to improve optimization performance by implementing a new mutation strategy ”DE/current-to-pbest” with optional external archive and updating control parameters in an adaptive manner. The DE/current-to- pbest is a generalization of the classic ”DE/current-to-best,” while the optional archive operation utilizes historical data to provide information of progress direction. Both operations diversify the population and improve the convergence performance. The parameter adaptation automatically updates the control parameters to appropriate values and avoids a user’s prior knowledge of the relationship between the parameter settings and the characteristics of optimization problems. It is thus helpful to improve the robustness of the algorithm. Simulation results show that JADE is better than, or at least comparable to, other classic or adaptive DE algorithms, the canonical particle swarm optimization, and other evolutionary algorithms from the literature in terms of convergence performance for a set of 20 benchmark problems. JADE with an external archive shows promising results for relatively high dimensional problems. In addition, it clearly shows that there is no fixed control parameter setting suitable for various problems or even at different optimization stages of a single problem.


References in zbMATH (referenced in 125 articles )

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  1. Chacón Castillo, Joel; Segura, Carlos: Differential evolution with enhanced diversity maintenance (2020)
  2. Chai, Xuzhao; Xiao, Junming; Zheng, Zhishuai; Zhang, Liang; Qu, Boyang; Yan, Li; et al.: UAV 3D path planning based on multi-population ensemble differential evolution (2020)
  3. Chen, Huiling; Wang, Mingjing; Zhao, Xuehua: A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems (2020)
  4. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: Optimization of expensive black-box problems via gradient-enhanced Kriging (2020)
  5. Chen, Xi; Wei, Qinqi: Optimal-operation model and optimization method for hybrid energy system on large ship (2020)
  6. Elaziz, Mohamed Abd; Li, Lin; Jayasena, K. P. N.; Xiong, Shengwu: Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution (2020)
  7. Gandhi, B. G. Rajeev; Bhattacharjya, R. K.: Differential evolution and its application in identification of virus release location in a sewer line (2020)
  8. Jonas Joacir Radtke; Guilherme Bertoldo; Carlos Henrique Marchi: DEPP - Differential Evolution Parallel Program (2020) not zbMATH
  9. Liao, Zuowen; Gong, Wenyin; Cai, Zhihua: A re-initialization clustering-based adaptive differential evolution for nonlinear equations systems (2020)
  10. Liu, Siwen; Liu, Xinbao; Pei, Jun; Pardalos, Panos M.; Song, Qingru: Parallel-batching machines scheduling problem with a truncated time-dependent learning effect via a hybrid CS-JADE algorithm (2020)
  11. Mohammed, Geleta T.; Aduda, Jane A.; Kube, Ananda O.: Improving forecasts of the EGARCH model using artificial neural network and fuzzy inference system (2020)
  12. Qu, Chiwen; He, Wei; Peng, Xiangni; Peng, Xiaoning: Harris Hawks optimization with information exchange (2020)
  13. Segredo, Eduardo; Lalla-Ruiz, Eduardo; Hart, Emma; Voß, Stefan: A similarity-based neighbourhood search for enhancing the balance exploration-exploitation of differential evolution (2020)
  14. Chen, Liming; Qiu, Haobo; Gao, Liang; Jiang, Chen; Yang, Zan: A screening-based gradient-enhanced Kriging modeling method for high-dimensional problems (2019)
  15. Huang, Qiujun; Zhang, Kai; Song, Jinchun; Zhang, Yimin; Shi, Jia: Adaptive differential evolution with a Lagrange interpolation argument algorithm (2019)
  16. Yang, Zan; Qiu, Haobo; Gao, Liang; Jiang, Chen; Zhang, Jinhao: Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems (2019)
  17. Zhang, Jinghua; Dong, Ze: Parameter combination framework for the differential evolution algorithm (2019)
  18. Céspedes-Mota, Armando; Castañón, Gerardo; Martínez-Herrera, Alberto F.; Cárdenas-Barrón, Leopoldo Eduardo: Multiobjective optimization for a wireless ad hoc sensor distribution on shaped-bounded areas (2018)
  19. Chen, Xu; Xu, Bin; Yu, Kunjie; Du, Wenli: Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering (2018)
  20. Du, Wei; Tong, Le; Tang, Yang: Metaheuristic optimization-based identification of fractional-order systems under stable distribution noises (2018)

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