反向學習相對基學習opposition-based learning簡介

反向學習，相對基學習opposition-based learning簡介

覺得有用的話,歡迎一起討論相互學習~

Opposition-based learning OBL

• 在Tizhoosh(2005)[1]中首次引入了OBL作爲一種新的計算智能方案。在過去的幾年裏，OBL已經成功地應用於各種基於種羣的進化算法中 [2]-[10]。衆所周知，從當前種羣中隨機生成一個解決方案，往往會導致重新訪問搜索空間中沒有希望的區域[11]-[12]，這是一種低效的探索模式。OBL的主要想法同時考慮候選的解決方案及其相反的解決方案。實驗表明,如果沒有先驗知識優化問題,相反的候選解決方案比隨機解能夠到達全局最優的概率更高[8]。因此，引入一個隨機解及其對應的反解比引入兩個獨立的隨機生成解更有希望。
• 在本文中，我們推廣了OBL的概念來解決MFO問題，並利用多任務環境中的多組上界和下界來產生相反的解決方案。
• 反解的數學定義如下:
  

參考資料
Liang, Z., Zhang, J., Feng, L. & Zhu, Z. A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking. Expert Systems with Applications 138, 112798 (2019).

[1]Tizhoosh, H. R. (2005). Opposition-based learning: A new scheme for machine in- telligence. In International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce: 1 (pp. 695–701). doi: 10.1109/CIMCA.2005. 1631345 .
[2]El-Abd, M. (2011). Opposition-based artificial bee colony algorithm. In Proceedings of the 13th annual conference on genetic and evolutionary computation (pp. 109–116). New York, NY, USA: ACM. doi: 10.1145/2001576.2001592 .
[3] Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2006). Opposition-based dif- ferential evolution for optimization of noisy problems. In 2006 IEEE international conference on evolutionary computation (pp. 1865–1872). doi: 10.1109/CEC.2006. 1688534 .
[4] Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2008a). Opposition-based dif- ferential evolution. IEEE Transactions on Evolutionary Computation, 12 (1), 64–79. doi: 10.1109/TEVC.20 07.89420 0 .
[5] Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2008b). Opposition versus ran- domness in soft computing techniques. Applied Soft Computing, 8 (2), 906–918. doi: 10.1016/j.asoc.2007.07.010 .
[6] Rahnamayan, S., Wang, G. G., & Ventresca, M. (2012). An intuitive distance-based explanation of opposition-based sampling. Applied Soft Computing, 12 (9), 2828–2839. doi: 10.1016/j.asoc.2012.03.034 .
[7] Wang, H., Li, H., Liu, Y., Li, C., & Zeng, S. (2007). Opposition-based particle swarm algorithm with cauchy mutation. In 2007 IEEE congress on evolutionary compu- tation (pp. 4750–4756). doi: 10.1109/CEC.2007.4425095 .
[8] Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., & Ventresca, M. (2011). Enhancing par- ticle swarm optimization using generalized opposition-based learning. Informa- tion Sciences, 181 (20), 4699–4714. doi: 10.1016/j.ins.2011.03.016 .
[9] Wang, W., Wang, H., Sun, H., & Rahnamayan, S. (2016). Using opposition-based learning to enhance differential evolution: A comparative study. In 2016 IEEE congress on evolutionary computation (pp. 71–77). doi: 10.1109/CEC.2016.7743780 .
[10] Zhou, Y., Hao, J., & Duval, B. (2017). Opposition-based memetic search for the max- imum diversity problem. IEEE Transactions on Evolutionary Computation, 21 (5), 731–745. doi: 10.1109/TEVC.2017.2674800
[11] Ma, X., Liu, F., Qi, Y., Gong, M., Yin, M., Li, L., . . . Wu, J. (2014). MOEA/D with opposition-based learning for multiobjective optimization problem. Neurocom- puting, 146 ©, 48–64. doi: 10.1016/j.neucom.2014.04.068 .
[12] Ma, X., Zhang, Q., Tian, G., Yang, J., & Zhu, Z. (2018). On Tchebycheffdecomposi- tion approaches for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 22 (2), 226–244. doi: 10.1109/TEVC.2017.2704118 .