Engineering Design Optimization Using an Advanced Hybrid Algorithm

Abstract

An advanced hybrid algorithm (haDEPSO) proposed in this paper for engineering design optimization problems. It integrated with suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO). In aDE introduced a novel mutation, crossover and selection strategy, to avoiding 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. Proposed hybrid haDEPSO as well as its integrating component aDE and aPSO has been applied to five engineering design optimization problems. Numerical, statistical and graphical experiments (best, worst, mean and standard deviation plus convergence analysis) confirm the superiority of the proposed algorithms over many state-of-the-art algorithms.