Earticle

초록

영어

Location problem of multi-distribution center is a kind of NP hard problem. To solve such problems, this paper proposes a chaos adaptive mutation particle swarm optimization algorithm. The algorithm uses the ergodic property of chaos to initialize the particle swarm to enhance the diversity of the population, according to the variance of population fitness to adjust the probability of mutation, and adjust the inertia weight factor to improve the global and local search capability of the whole population. In this paper, the algorithm is applied to the location problem of multi-distribution center, established the multi-factor constraints of mathematical model which aiming at timeliness, and on this basis, the corresponding algorithm is designed. It can be seen from the location instance simulation results that the optimization results and efficiency of the adaptive mutation particle swarm optimization algorithm is better than the genetic algorithm and the standard particle swarm optimization algorithm.

목차

Abstrac
 1. Introduction
 2. Mathematical Model of Multi -distribution distribution Center Location
  2.1. Problem Description
  2.2. Basic Assumptions
  2.3. Symbol Definition
  2.4. Model Building
 3. Particle Swarm Optimization Algorithm
  3.1. Standard PSO
  3.2. Chaos Adaptive Mutation Particle Swarm Optimization Algorithm
 4. Particle Swarm Optimization Algorithm for Multi-distribution Center Location Problem
  4.1. Particle Coding Design
  4.2. Algorithm Implementation Process
 5. Example Simulation
 4. Conclusions
 References

키워드

저자

• Tiaotiao Du [ School of Electronic and Information Engineering, LanZhou Jiao Tong University, LanZhou, China ]
• Kaijun Wu [ School of Electronic and Information Engineering, LanZhou Jiao Tong University, LanZhou, China ]
• Tiejun Wang [ School of mathematics and computer science institute, Northwest University for Nationalities, LanZhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605