Earticle

초록

영어

Particle swarm optimization algorithm is a species of intelligent algorithm, it can solve the problem of multiple end of decision making. But the algorithm is based on each group of particles would have been the effective information hypothesis. For most of the optimization problem, by the convergence speed, set the parameters of the limit, so this paper proposes a new more volume particle group algorithm. Crowding mechanism algorithm was applied to select group of particles in the process of the optimal value, thus maintaining the dispersion, the selection of the global optimal value is more reasonable. To introduce the concept of half a feasible region, and then to avoid the traditional processing method only considers particles in area the disadvantages of the boundary value processing precision is not high. In respect of time complexity, the grouping method is adopted to choose random switching strategy, improve the ef

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Particle Swarm Algorithm
  2.2. Multi-final Decision-Making Mechanism
 3. Improved the Final Amount of Particle Swarm Optimization Algorithm
 4. Experiment and Result Analysis
 5. Conclusion
 References

키워드

저자

• Fenghua Liu [ Shandong Women's University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605