A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization
A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization
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DOI：
10.1016/j.swevo.2020.100829
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Sanyi Li;Shengxiang Yang;Yanfeng Wang;Weichao Yue;J. Qiao
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文献类型：
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作者：
Sanyi Li;Shengxiang Yang;Yanfeng Wang;Weichao Yue;J. Qiao
提出了一种基于模块化神经网络（PA-MNN）的动态多目标优化种群预测算法。该算法由三个机制。首先，我们建立了一个模块化神经网络（MNN），并用历史人口信息对其进行训练。当检测到环境变化时，MNN生成一些初始解。第二，一些解决方案预测的基础上前瞻中心点。最后，一些解决方案是随机产生的，以保持多样性。通过这些机制，当以前遇到新的环境时，MNN生成的初始解将具有与上次在相同环境中获得的最终解相同的分布特征。由于基于MNN的初始化机制不需要最近时间的解，因此该算法也可以解决Pareto集变化剧烈且不规则的动态多目标优化问题。所提出的算法进行了测试的各种测试实例具有不同的动态特性和困难。实验结果表明，该算法在处理动态多目标优化问题上具有良好的应用前景。
This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization.