Multi-task optimization and evolutionary multitasking 文章快报

论文快报-2021-10-Multi-task optimization and evolutionary multitasking

A Multi-Variation Multifactorial Evolutionary Algorithm for Large-Scale Multi-Objective Optimization

• 传送门

摘要

• For solving large-scale multi-objective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem and performs the optimization in simplified spaces instead of the original problem space. Owing to the useful information provided by the simplified searching space, the performance of LSMOPs has been improved to some extent. However, it is worth noting that the original problem has changed after the variation, and there is thus no guarantee of the preservation of the original global or near-global optimum in the newly generated space. In this paper, we propose to solve LSMOPs via a multi-variation multifactorial evolutionary algorithm. In contrast to existing transformation-based methods, the proposed approach intends to conduct an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multi-variation manner concurrently. In this way, useful traits found along the search can be seamlessly transferred from the simplified problem spaces to the original problem space toward efficient problem-solving. Besides, since the evolutionary search is also performed in the original problem space, preserving the original global optimal solution can be guaranteed. To evaluate the performance of the proposed framework, comprehensive empirical studies are carried out on a set of LSMOPs with 2-3 objectives and 500-5000 variables. The experiment results highlight the efficiency and effectiveness of the proposed method compared to the state-of-the-art methods for large-scale multi-objective optimization.
• 对于解决大规模多目标问题（LSMOP），基于变换的方法显示出良好的搜索效率，将原始问题转换为新的简化问题，并在简化空间而不是原始问题空间中执行优化。由于简化的搜索空间提供了有用的信息，LSMOPs 的性能得到了一定程度的提高。然而，值得注意的是，原始问题在变异后发生了变化，因此无法保证在新生成的空间中保留原始全局或近全局最优。在本文中，我们建议通过多变量多因素进化算法来解决 LSMOP。与现有的基于变换的方法相比，所提出的方法旨在对 LSMOP 的原始空间和以多变量方式同时构建的多个简化空间进行进化搜索。通过这种方式，搜索过程中发现的有用特征可以从简化的问题空间无缝转移到原始问题空间，以实现高效的问题解决。此外，由于进化搜索也是在原始问题空间中进行的，所以可以保证保留原始的全局最优解。为了评估所提出框架的性能，对一组具有 2-3 个目标和 500-5000 个变量的 LSMOP 进行了全面的实证研究。与用于大规模多目标优化的最新方法相比，实验结果突出了所提出方法的效率和有效性。

Towards Generalized Resource Allocation on Evolutionary Multitasking for Multi-Objective Optimization

• 传送门

摘要

• Evolutionary multitasking optimization (EMTO) is an emerging paradigm for solving several problems simultaneously . Due to the flexible framework, EMTO has been naturally applied to multi-objective optimization to exploit synergy among distinct multi-objective problem domains. However, most studies barely take into account the scenario where some problems cannot converge under restrictive computational budgets with the traditional EMTO framework. T o dynamically allocate computational resources for multi-objective EMTO problems, this article proposes a generalized resource allocation (GRA) framework by concerning both theoretical grounds of conventional resource allocation and the characteristics of multi-objective optimization. normalized attainment function is designed for better quantifying convergence status, a multi-step nonlinear regression is proposed to serve as a stable performance estimator, and the algorithmic procedure of conventional resource allocation is refined for flexibly adjusting resource allocation intensity and including knowledge transfer information. It has been verified that the GRA framework can enhance the overall performance of the multi-objective EMTO algorithm in solving benchmark problems, complex problems, many-task problems, and a real-world application problem. Notably , the proposed GRA framework served as a crucial component for the winner algorithm in the Competition on Evolutionary Multi-T ask Optimization (Multi-objective Optimization Track) in IEEE 2020 W orld Congress on Computational Intelligence.
• 进化多任务优化（EMTO）是一种同时解决多个问题的新兴范式。由于其灵活的框架，EMTO已自然地应用于多目标优化，以利用不同多目标问题领域之间的协同作用。然而，大多数研究几乎没有考虑到一些问题在传统EMTO框架的限制性计算预算下无法收敛的情况。为了动态分配多目标EMTO问题的计算资源，本文结合传统资源分配的理论基础和多目标优化的特点，提出了广义资源分配（GRA）框架。为了更好地量化收敛状态，设计了归一化达到函数，提出了一种多步非线性回归作为稳定的性能估计器，改进了传统资源分配的算法流程，灵活调整资源分配强度，包含知识转移信息。经验证，GRA框架可以提高多目标EMTO算法在解决基准问题、复杂问题、多任务问题和实际应用问题时的整体性能。值得注意的是，在IEEE 2020世界计算智能大会的进化多任务优化（多目标优化轨道）竞赛中，所提出的GRA框架是胜利者算法的关键组成部分。

Improving Evolutionary Multitasking Optimization by Leveraging Inter-Task Gene Similarity and Mirror Transformation

• 传送门

摘要

• Solving a complex optimization task from scratch can be significantly expensive and/or time-consuming. Common knowledge obtained from different (but possibly related) optimization tasks may help enhance the solving of such tasks. In this regard, evolutionary multitasking optimization (EMTO) has been proposed to improve the solving of multiple optimization tasks simultaneously via knowledge transfer in the evolutionary algorithm framework. The effectiveness of knowledge transfer is crucial for the success of EMTO. Multifactorial evolutionary algorithm (MFEA) is one of the most representative EMTO algorithms, however, it suffers from negative knowledge transfer among the tasks with low correlation. To address this issue, in this study, inter-task gene-similarity-based knowledge transfer and mirror transformation are integrated into MFEA (termed as MFEA-GSMT). In the proposed inter-task gene-similarity-based knowledge transfer, a probabilistic model is used to feature each gene and the Kullback-Leibler divergence is employed to measure the inter-task dimension similarity. Guided by the inter-task gene similarity, a selective crossover is used to reproduce offspring solutions. The proposed inter-task knowledge transfer is based on online gene similarity evaluation, instead of individual similarity, to overcome the imprecise estimation of population distributions in a high-dimensional space with only a small number of samples. The proposed mirror transformation is an extension of opposition-based learning to avoid premature convergence and explore additional promising search areas. Experimental results on both single-objective and multi-objective multi-tasking problems demonstrate the effectiveness and efficiency of the proposed MFEA-GSMT.
• 从头开始解决复杂的优化任务可能非常昂贵和/或耗时。从不同（但可能相关）优化任务中获得的共同知识可能有助于增强此类任务的解决。在这方面，已经提出了进化多任务优化（EMTO），以通过进化算法框架中的知识转移来改进同时解决多个优化任务。知识转移的有效性对于 EMTO 的成功至关重要。多因子进化算法（MFEA）是最具代表性的 EMTO 算法之一，但它存在相关性低的任务之间的负知识转移问题。为了解决这个问题，在本研究中，基于任务间基因相似性的知识转移和镜像转换被集成到 MFEA（称为 MFEA-GSMT）中。在提出的基于任务间基因相似性的知识转移中，使用概率模型来表征每个基因，并采用 Kullback-Leibler 散度来衡量任务间维度相似性。在任务间基因相似性的指导下，使用选择性交叉来重现后代解决方案。所提出的任务间知识转移基于在线基因相似性评估，而不是个体相似性，以克服仅具有少量样本的高维空间中种群分布的不精确估计。提议的镜像转换是基于对立的学习的扩展，以避免过早收敛并探索其他有希望的搜索领域。单目标和多目标多任务问题的实验结果证明了所提出的 MFEA-GSMT 的有效性和效率。