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 80 articles )

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  1. Fan, Qinqin; Yan, Xuefeng; Zhang, Yilian: Auto-selection mechanism of differential evolution algorithm variants and its application (2018)
  2. Bujok, Petr: Improving the convergence of differential evolution (2017)
  3. Du, Wei; Miao, Qingying; Tong, Le; Tang, Yang: Identification of fractional-order systems with unknown initial values and structure (2017)
  4. 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)
  5. Lwin, Khin T.; Qu, Rong; MacCarthy, Bart L.: Mean-VaR portfolio optimization: a nonparametric approach (2017)
  6. 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)
  7. Quan, Haiyan; Shi, Xinling: A surface-simplex swarm evolution algorithm (2017)
  8. Wang, Xianpeng; Tang, Lixin: A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem (2017)
  9. Zhao, Fuqing; Shao, Zhongshi; Wang, Junbiao; Zhang, Chuck: A hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution (2017)
  10. 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)
  11. Cui, Laizhong; Li, Genghui; Lin, Qiuzhen; Chen, Jianyong; Lu, Nan: Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations (2016)
  12. Lieckens, Kris; Vandaele, Nico: Differential evolution to solve the lot size problem in stochastic supply chain management systems (2016)
  13. Li, Xiangtao; Yin, Minghao: Modified differential evolution with self-adaptive parameters method (2016)
  14. Mukherjee, Rohan; Debchoudhury, Shantanab; Das, Swagatam: Modified differential evolution with locality induced genetic operators for dynamic optimization (2016)
  15. Zhao, Zhiwei; Yang, Jingming; Hu, Ziyu; Che, Haijun: A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems (2016)
  16. Cheng, Ran; Jin, Yaochu: A social learning particle swarm optimization algorithm for scalable optimization (2015)
  17. Kundu, Souvik; Das, Swagatam; Vasilakos, Athanasios V.; Biswas, Subhodip: A modified differential evolution-based combined routing and sleep scheduling scheme for lifetime maximization of wireless sensor networks (2015) ioport
  18. Ouyang, Hai-bin; Gao, Li-qun; Kong, Xiang-yong; Zou, De-xuan; Li, Steven: Teaching-learning based optimization with global crossover for global optimization problems (2015)
  19. Segura, Carlos; Coello, Carlos A. Coello; Segredo, Eduardo; León, Coromoto: On the adaptation of the mutation scale factor in differential evolution (2015)
  20. Xu, Yulong; Fang, Jian-An; Zhu, Wu; Wang, Xiaopeng; Zhao, Lingdong: Differential evolution using a superior-inferior crossover scheme (2015)

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