Abstract

In decision-making, the multiobjective portfolio selection problem (MPSP) consists of the selection of alternatives based on preferences of a particular decision-maker (DM). In real-world applications, MPSP has several conflicting criteria that DMs must consider to determine an appropriate solution. So far, only fuzzy outranking relations have been used in a relational system of preferences to guide the search process of genetic algorithms (NOSGAII), and ant colony optimization (NOACO) to approximate the region of interest (RoI) of MPSP involving DM’s preferences. The NOSGAII and NOACO strategies are sequential, and they face a real challenge when solving high-dimensional instances which is a decrement in the computational efficiency due to the increment in the number of objectives. The present research proposes to use parallelism to tackle the efficiency situation in metaheuristics. The study first identifies which approach approximates better the RoI, and then, it analyzes the effect of parallelism in the performance. The results showed that NOACO found more best compromises in almost all the instances than NOSGAII. Hence, it can be concluded that NOACO approximates better the RoI. Also, the results showed a better average speedup with coarse-grained parallelism in NOACO than with data-flow parallelism, suggesting the conclusion that ants independently working are faster than ants working collaboratively. Finally, the main contributions are (1) the analysis of the performance of the two approaches for MPSP, (2) the five parallel designs for NOACO, and (3) the parallel NOACO that speedups up to 2× the sequential version when solving MPSP.

中文翻译：

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元启发式方法的并行设计，可使用模糊排名关系解决投资组合选择问题

摘要

在决策中，多目标投资组合选择问题（MPSP）包括根据特定决策者（DM）的偏好来选择备选方案。在实际应用中，MPSP有几个相互矛盾的标准，DM必须考虑这些标准来确定适当的解决方案。到目前为止，在偏好的关系系统中仅使用模糊的排名关系来指导遗传算法（NOSGAII）的搜索过程，并使用蚁群优化（NOACO）来估计涉及DM偏好的MPSP的感兴趣区域（RoI）。NOSGAII和NOACO策略是顺序的，它们在解决高维实例时面临着真正的挑战，由于目标数量的增加，计算效率下降了。本研究建议使用并行性来解决元启发式方法中的效率情况。该研究首先确定哪种方法更接近RoI，然后分析并行性对性能的影响。结果表明，与NOSGAII相比，NOACO在几乎所有情况下都发现了更多的最佳折衷方案。因此，可以得出结论，NOACO的RoI更好。而且，结果表明，与数据流并行性相比，NOACO中的粗粒度并行性具有更好的平均加速，这表明了独立工作的蚂蚁比协同工作的蚂蚁更快的结论。最后，主要贡献是（1）对两种MPSP方法的性能进行分析，（2）对NOACO的五种并行设计，