增量式支持向量机求解动态多目标优化问题
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
摘要：
动态多目标优化问题的主要特点是优化目标函数会随时间或环境而变化。解决DMOP问题的一种有前途的方法是通过机器学习方法重用获得的Pareto最优集（POS）来训练预测模型。本文利用过去的POS对增量式支持向量机（ISVM）分类器进行训练，然后通过训练好的ISVM分类器对下一时刻要求解的DMOP的解进行筛选。ISVM分类器将产生高质量的初始种群，并且各种不同类型的基于种群的动态多目标优化算法可以受益于该种群。为了验证这一想法，我们将所提出的方法融入到三种进化算法中，即多目标粒子群优化算法（MOPSO）、非支配排序遗传算法II（NSGA-II）和基于规则性模型的多目标分布估计算法（RE-MEDA）。我们通过实验对这些算法进行了测试，实验结果表明了算法的有效性。
英文摘要：
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments. One of the promising approaches for solving the DMOPs is reusing the obtained Pareto optimal set (POS) to train prediction models via machine learning approaches. In this paper, we train an Incremental Support Vector Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP we want to solve at the next moment are filtered through the trained ISVM classifier. A high-quality initial population will be generated by the ISVM classifier, and a variety of different types of population-based dynamic multi-objective optimization algorithms can benefit from the population. To verify this idea, we incorporate the proposed approach into three evolutionary algorithms, the multi-objective particle swarm optimization(MOPSO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We employ experiments to test these algorithms, and experimental results show the effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
