Invasive Weed Optimization (IWO) is a recently developed derivative-free metaheuristic algorithm that mimics the robust process of weeds colonization and distribution in an ecosystem. On the other hand central to an ecosystem is the foraging behavior that pertains to the act of searching for food and forms an integral part of the daily life of most of the living creatures. For over past two decades, a few significant optimization algorithms were developed by emulating the foraging behavior of creatures like ants, bacteria, fish, bees etc. This article presents a hybrid real-parameter optimizer developed by incorporating the principles of Optimal Foraging Theory (OFT) in IWO, with a view to improving the search mechanism of the latter over discontinuous and multi-modal fitness landscapes, riddled with local optima. The hybridization does not impose any serious computational burden on IWO in terms of increasing number of Function Evaluations (FEs). The performance of the resulting hybrid algorithm has been compared with eleven other state-of-the-art metaheuristic algorithms over a test-suite of 16 numerical benchmarks taken from the CEC (Congress on Evolutionary Computation) 2005 competition and special session on real parameter optimization. Our simulation experiments indicate that the proposed algorithm is able to attain comparable results against the nine other optimizers. Owing to its promising performance on benchmarks and ease of implementation (without requiring much programming overhead), the proposed algorithm may serve as an attractive alternative for a plethora of practical optimization problems.