Dehghani Mohammad, Trojovský Pavel
Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Kralove, Czech Republic.
Sensors (Basel). 2022 Feb 24;22(5):1795. doi: 10.3390/s22051795.
With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm's key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA's performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.
随着科学技术的进步，出现了新的复杂优化问题，实现最优解变得越来越重要。这些问题中的许多都具有非凸、非线性、离散搜索空间和不可微目标函数等特征和难点。实现此类问题的最优解已成为一项重大挑战。为应对这一挑战并提供一种解决优化应用复杂性和难点的方法，本研究提出了一种基于随机的新优化算法。优化算法是一种用于解决优化问题的随机方法，它通过对搜索空间进行随机扫描来产生准最优解。基于选择一些变量进行更新的算法（SSVUBA）是本研究开发的一种新优化算法，用于处理各个领域的优化问题。所提算法的关键原则是更好地利用种群中不同个体提供的信息，并在算法迭代过程中调整用于更新算法种群的变量数量。阐述了所提SSVUBA的理论，然后给出其数学模型以用于解决优化问题。利用包括单峰、多峰和CEC 2017测试函数在内的53个目标函数来评估所提SSVUBA解决优化问题的能力和实用性。在四个工程设计问题上评估了SSVUBA在优化实际应用方面的性能。此外，将SSVUBA在优化方面的性能与八种知名算法的性能进行比较，以进一步评估其质量。仿真结果表明，所提SSVUBA具有处理各种优化问题的显著能力，并且通过给出更接近全局最优解的适当准最优解，其性能优于其他竞争算法。
