This paper designed an advanced hybrid algorithm (haDEPSO) to solve the optimization problems, based on multi-population approach. It integrated with suggested advanced DE (aDE) and PSO (aPSO). Where in aDE a novel mutation strategy and crossover probability along with the slightly changed selection scheme are introduced, to avoid premature convergence. And aPSO consists of the novel gradually varying inertia weight and acceleration coefficient parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The algorithms efficiency is verified through 23 basic, 30 CEC 2014 and 30 CEC 2017 test suite and comparing the results with various state-of-the-art algorithms. The numerical, statistical and graphical analysis shows the effectiveness of these algorithms in terms of accuracy and convergence speed. Finally, three real world problems have been solved to confirm problem-solving capability of proposed algorithms. All these analyses confirm the superiority of the proposed algorithms over the compared algorithms.

本文设计了一种基于多种群方法的高级混合算法（h aDEPSO）来解决优化问题。它与建议的高级DE（aDE）和PSO（aPSO）集成。在aDE中，引入了一种新颖的变异策略和交叉概率以及稍微改变的选择方案，以避免过早收敛。而aPSO则包括新颖的，逐渐变化的惯性权重和加速度系数参数，可以避免停滞。因此，aDE和aPSO的收敛特性为解空间提供了不同的近似值。因此，^ h aDEPSO实现因整合ADE和APSO的优点更好的解决方案。也在haDEPSO个人人口以预定的方式与其他人合并，以在全局搜索能力和本地搜索能力之间取得平衡。通过23个基本的，30个CEC 2014和30个CEC 2017测试套件，并将结果与​​各种最新算法进行比较，可以验证算法的效率。数值，统计和图形分析显示了这些算法在准确性和收敛速度方面的有效性。最后，解决了三个现实问题，以确认所提出算法的问题解决能力。所有这些分析证实了所提出的算法相对于比较算法的优越性。