Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Quantum dynamic mechanism-based multi-objective evolutionary algorithm and performance analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xiaoming You ; Coll. of Electron. & Electr. Eng, Shanghai Univ. of Eng. Sci., Shanghai, China ; Xiankun Sun ; Sheng Liu ; Jiaying Huang

A novel Self-organizing Quantum Evolutionary Algorithm for Multi-objective optimization(MSQEA) is proposed. The technique for improving the performance of MSQEA has been described. By using self-organizing co-evolution strategy each subpopulation can obtain more optimal solutions. Because of the quantum dynamic mechanism all the subpopulations may move concurrently in a force-field until all of them reach their equilibrium states. Self-organizing algorithm has advantages in terms of the adaptability; reliability and the learning ability over traditional organizing algorithm, so the solution quality is improved. 0/1 Multi-objective knapsack problem simulation results demonstrate the superiority of MSQEA in this paper.

Published in:

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on  (Volume:1 )

Date of Conference:

20-22 Nov. 2009