求解多目标最优化的混合自适应进化算法
Hybrid Adaptive Evolutionary Algorithm for Multi-objective Optimization
摘要：
多目标最优化进化算法的主要困难是如何找到一个合适的解，该解能够以高多样性收敛到真正的Pareto前沿。大多数现有的方法，这已经证明了他们的利基对各种实际问题，涉及两个和三个目标，面临着重大的挑战，在依赖的EA参数的选择。此外，设置这样的参数的过程被认为是耗时的，并且一些研究工作试图处理这个问题。提出了一种新的多目标优化算法MoHAEA。MoHAEA允许动态适应的应用程序的运营商的概率（率）演变的多目标问题的解决方案相结合的优势和分解为基础的方法。在10个广泛使用的多目标测试问题上，将MoHAEA与四种最先进的MOEA，即MOEA/D，pa $λ$ -MOEA/D，MOEA/D-AWA和NSGA-II进行了比较。实验结果表明，MoHAEA优于基准算法，它是如何能够找到一个覆盖良好，分布良好的一组点的Pareto阵线。
英文摘要：
The major difficulty in Multi-objective Optimization Evolutionary Algorithms (MOEAs) is how to find an appropriate solution that is able to converge towards the true Pareto Front with high diversity. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in the dependency of the selection of the EA parameters. Moreover, the process of setting such parameters is considered time-consuming, and several research works have tried to deal with this problem. This paper proposed a new Multi-objective Algorithm as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA) called MoHAEA. MoHAEA allows dynamic adaptation of the application of operator probabilities (rates) to evolve with the solution of the multi-objective problems combining the dominance- and decomposition-based approaches. MoHAEA is compared with four states of the art MOEAs, namely MOEA/D, pa$λ$-MOEA/D, MOEA/D-AWA, and NSGA-II on ten widely used multi-objective test problems. Experimental results indicate that MoHAEA outperforms the benchmark algorithms in terms of how it is able to find a well-covered and well-distributed set of points on the Pareto Front.
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