A modified invasive weed optimization with crossover operation

Invasive weed optimization, which is inspired from the invasive habits of growth of weeds in nature, is a population-based intelligence algorithm. In this paper, we present invasive weed optimization with crossover operation combining the idea of the invasive weed with concepts from evolutionary algorithms. By applying the crossover operation in invasive weed optimization, it not only discourages premature convergence to local optimum but also explores and exploits the promising regions in the search space effectively. This modified algorithm is tested and compared with the standard invasive weed optimization and PSO. The comparative experiments have been conducted on benchmark test functions; invasive weed optimization with crossover operation is able to obtain the result superior to the standard invasive weed optimization and PSO.