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 88 articles )

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  1. Du, Wei; Tong, Le; Tang, Yang: Metaheuristic optimization-based identification of fractional-order systems under stable distribution noises (2018)
  2. Fan, Qinqin; Yan, Xuefeng; Zhang, Yilian: Auto-selection mechanism of differential evolution algorithm variants and its application (2018)
  3. Qian, Shuqu; Ye, Yongqiang; Liu, Yanmin; Xu, Guofeng: An improved binary differential evolution algorithm for optimizing PWM control laws of power inverters (2018)
  4. Bujok, Petr: Improving the convergence of differential evolution (2017)
  5. Du, Wei; Miao, Qingying; Tong, Le; Tang, Yang: Identification of fractional-order systems with unknown initial values and structure (2017)
  6. Lin, Qiuzhen; Tang, Chaoyu; Ma, Yueping; Du, Zhihua; Li, Jianqiang; Chen, Jianyong; Ming, Zhong: A novel adaptive control strategy for decomposition-based multiobjective algorithm (2017)
  7. Lwin, Khin T.; Qu, Rong; MacCarthy, Bart L.: Mean-VaR portfolio optimization: a nonparametric approach (2017)
  8. Pierezan, Juliano; Freire, Roberto Zanetti; Weihmann, Lucas; Reynoso-Meza, Gilberto; dos Santos Coelho, Leandro: Static force capability optimization of humanoids robots based on modified self-adaptive differential evolution (2017)
  9. Quan, Haiyan; Shi, Xinling: A surface-simplex swarm evolution algorithm (2017)
  10. Wang, Xianpeng; Tang, Lixin: A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem (2017)
  11. Zhao, Fuqing; Shao, Zhongshi; Wang, Junbiao; Zhang, Chuck: A hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution (2017)
  12. Azad, Nasser L.; Mozaffari, Ahmad; Vajedi, Mahyar; Masoudi, Yasaman: Chaos oscillator differential search combined with Pontryagin’s minimum principle for simultaneous power management and component sizing of PHEVs (2016)
  13. Cui, Laizhong; Li, Genghui; Lin, Qiuzhen; Chen, Jianyong; Lu, Nan: Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations (2016)
  14. Khanum, Rashida Adeeb; Jan, Muhammad Asif; Tairan, Nasser Mansoor; Mashwani, Wali Khan: Hybridization of adaptive differential evolution with an expensive local search method (2016)
  15. Kiran, Deep; Panigrahi, B. K.; Das, Swagatam; Kumar, Nitesh: Linkage based deferred acceptance optimization (2016)
  16. Lieckens, Kris; Vandaele, Nico: Differential evolution to solve the lot size problem in stochastic supply chain management systems (2016)
  17. Li, Xiangtao; Yin, Minghao: Modified differential evolution with self-adaptive parameters method (2016)
  18. Li, Zongyan; Li, Deliang: An improved global harmony search algorithm for the identification of nonlinear discrete-time systems based on Volterra filter modeling (2016)
  19. Mukherjee, Rohan; Debchoudhury, Shantanab; Das, Swagatam: Modified differential evolution with locality induced genetic operators for dynamic optimization (2016)
  20. Peng, Hu; Wu, Zhijian; Shao, Peng; Deng, Changshou: Dichotomous binary differential evolution for knapsack problems (2016)

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