Neural Computing and Applications

, Volume 27, Issue 8, pp 2429–2440 | Cite as

A two-stage quantum-behaved particle swarm optimization with skipping search rule and weight to solve continuous optimization problem

  • Deyu Tang
  • Shoubin Dong
  • Xianfa Cai
  • Jie Zhao
Original Article


Quantum-behaved particle swarm optimization (QPSO) is a recently developed heuristic method by particle swarm optimization (PSO) algorithm based on quantum mechanics, which outperforms the search ability of original PSO. But as many other PSOs, it is easy to fall into the local optima for the complex optimization problems. Therefore, we propose a two-stage quantum-behaved particle swarm optimization with a skipping search rule and a mean attractor with weight. The first stage uses quantum mechanism, and the second stage uses the particle swarm evolution method. It is shown that the improved QPSO has better performance, because of discarding the worst particles and enhancing the diversity of the population. The proposed algorithm (called ‘TSQPSO’) is tested on several benchmark functions and some real-world optimization problems and then compared with the PSO, SFLA, RQPSO and WQPSO and many other heuristic algorithms. The experiment results show that our algorithm has better performance than others.


Particle swarm optimization Quantum behavior Two-stage search strategy Skipping search rule Weighted mean best position 



This work was supported by NSFC, research on reasoning of behavior trust for resisting collusive reputation attack (71401045); the National Natural Science Foundation Project (No. 61070092/F020504); the building of strong Guangdong Province for Chinese medicine scientific research(20141165); the Humanities and social science fund project for Guangdong Pharmaceutical University (RWSK201409); GuangDong Provincial Natural fund (2014A030313585), Ukraine Senate Xingnao neuroprotective effect mechanism of dynamic network based on network pharmacology; Guangdong Province Youth Innovation Talent Project, based on the cognitive rules of the semi-supervised key algorithm and its cancer pattern recognition (2014KQNCX139); 2014 annual training program of outstanding young teachers in Higher Education in Guangdong Province; Guangdong Science & Technology Projects (2013B090500087/2014B010112006).


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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  • Deyu Tang
    • 1
    • 2
  • Shoubin Dong
    • 2
  • Xianfa Cai
    • 1
  • Jie Zhao
    • 3
  1. 1.School of Medical Information and EngineeringGuangDong Pharmaceutical UniversityGuangzhouChina
  2. 2.School of Computer Science & EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.Department of information Management Engineering, School of ManagementGuangdong University of TechnologyGuangzhouChina

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