Improved Quantum Particle Swarm Optimization for Mangroves Classification

Abstract

Quantum particle swarm optimization (QPSO) is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

“…Based on this, the regulation parameter i  of the i -th particle is the key factor to balance the influence of the local best and global best. Therefore, the acceleration factors are introduced to generate the regulation parameter in the updating of the local attractor, which are given by ( 5) in the following [93],…”

“…Therefore, the diversity function, which is denoted by D , is introduced to dynamically adjust the acceleration factors in order to avoid prematurity and to enhance the global search ability. The diversity function is shown in ( 6) in the following [93],…”

“…avg f is the average of fitness values for all the current position of the particles. Thus the maximum and minimum of diversity function D is 0 and 1 through Equation ( 6), especially if 1 D  , which means the population diversity of the particles is poor, and if 1 D  , which means the population diversity of particles is good [93].…”

“…The diversity function D , as we mentioned above, is introduced to dynamically adjust the values of the corresponding acceleration factor 1 c and 2 c , which is based on the proximity of the current position and global best in the population. The corresponding acceleration factors 1 c and 2 c are given by ( 7) in the following [93],…”

“…Quantum-behaved particle swarm optimization (QPSO) is a variant of PSO which was initiated by Sun in 2004 [92], and the sampling space of QPSO covers the whole search space due to the probabilities of potential well models [93]. It has been proved that the global search ability of QPSO has been improved more than classic PSO [94].…”