Earticle

초록

영어

The quantum particle swarm optimization (QPSO) algorithm exists some defects, such as premature convergence, poor search ability and easy falling into local optimal solutions. The adaptive adjustment strategy of inertia weight, chaotic search method and neighborhood mutation strategy are introduced into the QPSO algorithm in order to propose an improved quantum particle swarm optimization (AMCQPSO) algorithm in this paper. In the AMCQPSO algorithm, the chaotic search method is employed to promote the quality of initial population. The adaptive adjustment strategy of inertia weight is used to adjust the global search ability and local search ability of particles in the running process of QPSO algorithm. The neighborhood mutation strategy is used to increase the diversity of population and avoid premature convergence. Finally, in order to evaluate the performance of the AMCQPSO algorithm, several well-known benchmark functions are selected in this paper. The experiment simulations show that the proposed AMCQPSO algorithm can effectively improve the quality of solutions, and takes on powerful optimizing ability and more quickly convergence speed.

목차

Abstract
 1. Introduction
 2. Particle Swarm Optimization (PSO) Algorithm
 3. Quantum Particle Swarm Optimization (QPSO) Algorithm
 4. Improved Quantum Particle Swarm Optimization (AMCQPSO) Algorithm
  4.1. Chaotic Search Method
  4.2. Adaptive Adjustment Strategy of Inertia Weight
  4.3. Neighborhood Mutation Strategy
 5. The Describing of AMCQPSO Algorithm
 6. Experiment Results and Analysis
 7. Conclusion
 References

키워드

저자

• Lei Wang [ Department of Mathematics, Binzhou University, Binzhou Shandong 256600 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605