An Improved Quantum Evolutionary Algorithm Based on Invasive Weed Optimization

This paper presents a new optimization algorithm based on developing the invasive weed optimization algorithm by the quantum computing. It is characterized by a representation of quantum seeds and their competition to survive. Its advantages lie on the reaching to the global minimum in less iteration. Some standard functions are analyzed with the proposed algorithm and the final results are compared by the standard invasive weed optimization algorithm. The simulation results show the excellence of quantum based invasive weed optimization algorithm toward the standard type.

Hybrid Meta-heuristic algorithm, Invasive Weed Optimization, Global Optimization, Quantum Behavior.