In most optimization problems, the solution space is very vast. At the same time, the imposed constraints and limitations provide additional burdens for the underline algorithm. Although hybrid intelligent approaches have proven their potential in demanding problem settings, in most cases, they manage to approximate a good-quality solution, meaning that there is room for improvement. Based on preliminary findings from previous studies, the aim of this paper is to highlight the impact of the incorporation of heuristic information, i.e. expert’s knowledge etc., to the overall performance of a hybrid Nature-Inspired algorithm. Experimental findings support the related results from HAIS 2017 conference [Tzanetos, Vassiliadis and Dounias, 2017, Lecture Notes in Computer Science, 10334]. More specifically, under the framework of a financial portfolio optimization problem, the heuristic information-enhanced hybrid algorithm manages to reach a new optimal solution. What is more, in the scope of Nature-Inspired algorithms, some preliminary results, under the framework of hybrid algorithms, from a newly proposed metaheuristic are provided, though further experimentation and fine-tuning are required.

中文翻译：

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提高混合自然灵感算法的性能：来自财务优化领域的应用

在大多数优化问题中，解决方案空间很大。同时，强加的约束和限制给下划线算法带来了额外的负担。尽管混合智能方法已经证明了其在苛刻问题设置中的潜力，但在大多数情况下，它们设法逼近优质解决方案，这意味着仍有改进的空间。基于先前研究的初步发现，本文的目的是强调引入启发式信息（即专家的知识等）对混合自然启发算法的整体性能的影响。实验结果支持了HAIS 2017大会的相关结果[Tzanetos，Vassiliadis和Dounias，2017年，计算机科学讲座，10334]。进一步来说，在金融投资组合优化问题的框架下，启发式信息增强混合算法设法达到了新的最优解。此外，在自然启发式算法的范围内，尽管需要进一步的实验和微调，但在混合算法的框架下，从新提出的元启发式算法中提供了一些初步结果。