An advanced hybrid algorithm for constrained function optimization with engineering applications

An advanced hybrid algorithm (haDEPSO) is proposed in this paper for constrained optimization problems, based on a multi-population approach. It integrated with suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO). In aDE a novel mutation strategy, crossover probability and random nature selection scheme are introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, to avoid stagnation. The convergence characteristic of aDE and aPSO provides a different approximation to the solution space. Thus, haDEPSO achieves 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 hybrid and its integrated component is verified on IEEE CEC2006 and IEEE CEC2010 constrained benchmark functions plus five complex engineering problems. Several numerical, statistical, graphical and comparative analyses confirm superiority of proposed algorithms over many state-of-the-art algorithms.