Quantum-Behaved Bat Algorithm Combining Convergence Factor and Self-Learning Mutation Strategies for Optimization

Abstract

Quantum-behaved Bat Algorithm (QBA) has been successfully applied as an optimal technique for dealing with a variety of optimization problems. Nevertheless, QBA suffers from similar problems as other swarm intelligent algorithms, such as poor exploration search and falling into local optima in certain conditions. Aiming at these shortcomings, an improved algorithm that combines convergence factor and gold sinusoidal self-learning mutation strategies (CGQBA) is proposed. A directional convergence factor is designed for the global position update process, it can improve the exploration search ability of the algorithm. Meanwhile, a self-learning predictive mutation mechanism is added to the algorithm. It contributes to the algorithm to jump out of the local extremum. The improved CGQBA algorithm is tested on 20 test functions with different characteristics in the numerical simulation experiments. The results and statistical tests show that CGQBA algorithm has better convergence speed, accuracy and stability. What is more, the multi-threshold image segmentation is modelled as an optimization problem, CGQBA algorithm is applied to the optimization problem to further verify the effectiveness and practicability in the real-world optimization. The results compared with three classical segmentation methods illustrate that CGQBA algorithm can effectively solve the image segmentation problem. It has a better segmentation effect and anti-noise ability.