反向学习相对基学习opposition-based learning简介

反向学习，相对基学习opposition-based learning简介

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Opposition-based learning OBL

• 在Tizhoosh(2005)[1]中首次引入了OBL作为一种新的计算智能方案。在过去的几年里，OBL已经成功地应用于各种基于种群的进化算法中 [2]-[10]。众所周知，从当前种群中随机生成一个解决方案，往往会导致重新访问搜索空间中没有希望的区域[11]-[12]，这是一种低效的探索模式。OBL的主要想法同时考虑候选的解决方案及其相反的解决方案。实验表明,如果没有先验知识优化问题,相反的候选解决方案比随机解能够到达全局最优的概率更高[8]。因此，引入一个随机解及其对应的反解比引入两个独立的随机生成解更有希望。
• 在本文中，我们推广了OBL的概念来解决MFO问题，并利用多任务环境中的多组上界和下界来产生相反的解决方案。
• 反解的数学定义如下:
  

参考资料
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).

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[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 .
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[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 .
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[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 .