A hybrid version of invasive weed optimization with quadratic approximation

Invasive weed optimization (IWO) is a recent meta-heuristic optimization technique, based on the life cycle of plants. It has been applied in many engineering applications as well as in real world problems. In this paper, a hybrid version of IWO with the quadratic approximation (QA) operator, referred as QAIWO, has been investigated to improve the convergence rate of IWO while obtaining optimal solution. Additionally, we alleviate the limitation of QA (which is nothing but difficulty in escaping from a local optimum) by performing QA a predetermined number of times and then considering the average of all such solutions due to each iteration rather than a single solution. This technique makes our algorithm more efficient compared to the existing algorithms in the area. Twenty two benchmark problems and five real-life problems are adopted from literature to validate our proposed hybrid method QAIWO. The results of QAIWO are compared with the results obtained by the standard IWO and the well-known nature-inspired genetic algorithm (GA). These comparisons exhibit that QAIWO is more convenient to solve complex problems than using IWO and/or GA.