Artificial Bee Colony Algorithm Based on Quantum Bloch Spherical Optimization

Abstract

The swarm intelligence optimization algorithm has strong adaptability to optimization problems, fast computational speed, and the ability to quickly find the optimal solution, demonstrating a momentum of rapid development. As a type of swarm intelligence optimization algorithm, artificial bee colony algorithm obtains the optimal solution through the cyclic foraging and iteration of hired bees, observation bees, and reconnaissance bees, and has now been widely applied in various fields, But the artificial bee colony algorithm has the shortcomings of slow Rate of convergence and easy to fall into the local optimal solution. To overcome these shortcomings, this paper proposes an artificial bee colony algorithm based on the quantum Bloch spherical optimization mechanism. The effectiveness of the algorithm is proved through six benchmark test functions. The experimental results of the convergence curve graph can show that the Rate of convergence is greatly accelerated, and the global optimal solution can be obtained quickly.