Improved Quantum Particle Swarm Optimization for Mangroves Classification

Zhehuang Huang

doi:10.1155/2016/9264690

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.

Highlights

Quantum particle swarm optimization algorithm (QPSO) is a new evolutionary algorithm proposed in 2005 [1, 2]
In [12], chaotic mutation operator is introduced to quantum particle swarm optimization, instead of random sequences in QPSO; chaotic mutation operator is a powerful strategy to diversify the QPSO population and can improve the performance in preventing premature convergence to local minima
In [13], an improved quantum particle swarm optimization algorithm based on real coding method is presented which can improve the performance of QPSO

Summary

Introduction

Quantum particle swarm optimization algorithm (QPSO) is a new evolutionary algorithm proposed in 2005 [1, 2]. In [12], chaotic mutation operator is introduced to quantum particle swarm optimization, instead of random sequences in QPSO; chaotic mutation operator is a powerful strategy to diversify the QPSO population and can improve the performance in preventing premature convergence to local minima. In [13], an improved quantum particle swarm optimization algorithm based on real coding method is presented which can improve the performance of QPSO. Adaptive movement behavior is introduced into quantum particle swarm optimization for the first time. To effectively avoid the occurrence of premature phenomenon, D(t) can be used to describe the closer degree between particles and the global optimal position, so as to select the corresponding acceleration factor:. (8) If the iteration is terminated, output the optimal value; otherwise return to Step (3)

An Improved SNN Model Based on AQPSO

Experiment

Recognition of Single Mangrove Image

C7 C8 C9 C10

Conclusions