An analytical optimization algorithm based on quantum computing embedded into evolutionary algorithm

Optimization is one of the most primary jobs of all the engineering problems. We are fortunate to have blessed with scores of methods available for optimization purpose. Evolutionary algorithm is fast becoming one of the most sought after methods for this purpose. This method depends upon random moves. So sometimes the time consumed by this algorithm is much more than expected. With an aim to get rid of this problem, in this paper we propose an entirely new approach of evolutionary algorithm based on quantum computing. A quantum evolutionary algorithm can be termed as a concept which utilizes certain tools of quantum computing for search and optimization purposes. A quantum algorithm relies heavily on qubits and quantum gates. Qubits are the entities which actually contain information in quantum computing. They can either remain in a secluded state or a superposition of two or more qubits states. When they are in a superposition state we employ the concepts of probability to predict where they can actually be found. On the other hand quantum gates utilize qubits to achieve some predefined goals. These predefined goals in case of an optimization problem are to maximize or minimize some sort of objective function. This paper proposes a novel evolutionary algorithm employing quantum computing to address optimization problems. We have tested our algorithm on a famous problem known as Gear Train Design Problem. The results so obtained outperform the classical optimization methods.