An advanced hybrid algorithm (haDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) and particle swarm optimization (aPSO) integrated with proposed haDEPSO. In aDE a novel, mutation, crossover and selection strategy is introduced, to avoid premature convergence. And aPSO consists of novel gradually varying 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 performance of proposed haDEPSO and its component aDE and aPSO are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) and one large (economic load dispatch)-scale engineering design optimization problems. Outcome analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.

中文翻译：

---

解决小规模和大规模工程设计优化问题的高级混合元启发式算法

针对小型和大型工程设计优化问题，本文提出了一种先进的混合算法（haDEPSO）。与建议的haDEPSO集成的建议的高级差分进化（aDE）和粒子群优化（aPSO）。在aDE中，引入了一种新颖的变异，交叉和选择策略，以避免过早收敛。而且aPSO由新颖的逐渐变化的参数组成，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，由于整合了aDE和aPSO的优点，haDEPSO实现了更好的解决方案。同样在haDEPSO中，单个种群以预定的方式与其他种群合并，以在全局搜索能力和本地搜索能力之间取得平衡。在23个不受约束的基准功能上验证了拟议的haDEPSO及其组件aDE和aPSO的性能，然后解决了五个小型（结构工程）和一个大型（经济负荷分配）规模的工程设计优化问题。结果分析证实了所提出的算法优于许多最新算法的优势。