论文标题
一种新型的元元素优化算法，灵感来自病毒的传播
A Novel Meta-Heuristic Optimization Algorithm Inspired by the Spread of Viruses
论文作者
论文摘要
根据无需亮起的定理，没有一个单个元式算法可以最佳地解决所有优化问题。这激发了许多研究人员不断开发新的优化算法。在本文中，提出了一种新型的自然风格的元式优化算法，称为病毒扩散优化（VSO）。 VSO松散地模仿病毒在宿主之间的传播，可以有效地用于解决许多具有挑战性和持续的优化问题。我们设计了一种新的表示方案和病毒操作，该方案与先前提出的基于病毒的优化算法截然不同。首先，VSO中每个宿主的病毒RNA表示一个潜在的解决方案，该解决方案不同的病毒操作将有助于多样化搜索策略，从而在很大程度上提高溶液质量。此外，引入了一种进口感染机制，即从另一个菌落中继承搜索的Optima，以避免在解决复杂问题方面的任何潜在解决方案的过度。 VSO具有出色的功能，可以在发现的Optima周围进行自适应邻里搜索，以实现更好的解决方案。此外，使用灵活的感染机制，VSO可以迅速从当地的Optima逃脱。为了清楚地证明其有效性和效率，VSO对一系列众所周知的基准功能进行了严格评估。此外，VSO通过两个现实世界的示例对其适用性进行了验证，包括用于分类问题的支持向量机的超参数的财务组合优化和优化。结果表明，与传统和最先进的元元素优化算法相比，VSO在解决方案适应性，收敛率，可伸缩性，可靠性和灵活性方面取得了出色的性能。
According to the no-free-lunch theorem, there is no single meta-heuristic algorithm that can optimally solve all optimization problems. This motivates many researchers to continuously develop new optimization algorithms. In this paper, a novel nature-inspired meta-heuristic optimization algorithm called virus spread optimization (VSO) is proposed. VSO loosely mimics the spread of viruses among hosts, and can be effectively applied to solving many challenging and continuous optimization problems. We devise a new representation scheme and viral operations that are radically different from previously proposed virus-based optimization algorithms. First, the viral RNA of each host in VSO denotes a potential solution for which different viral operations will help to diversify the searching strategies in order to largely enhance the solution quality. In addition, an imported infection mechanism, inheriting the searched optima from another colony, is introduced to possibly avoid the prematuration of any potential solution in solving complex problems. VSO has an excellent capability to conduct adaptive neighborhood searches around the discovered optima for achieving better solutions. Furthermore, with a flexible infection mechanism, VSO can quickly escape from local optima. To clearly demonstrate both its effectiveness and efficiency, VSO is critically evaluated on a series of well-known benchmark functions. Moreover, VSO is validated on its applicability through two real-world examples including the financial portfolio optimization and optimization of hyper-parameters of support vector machines for classification problems. The results show that VSO has attained superior performance in terms of solution fitness, convergence rate, scalability, reliability, and flexibility when compared to those results of the conventional as well as state-of-the-art meta-heuristic optimization algorithms.
