Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion
Zhou, A, Zhang, Q, Tsang, E, Jin, Y and Sendhoff, B (2006) Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion
![]() |
Text
cec06ZJZ.pdf Restricted to Repository staff only Available under License : See the attached licence file. Download (259kB) |
![]() |
Text (licence)
SRI_deposit_agreement.pdf Restricted to Repository staff only Download (33kB) |
Abstract
In our previous work [1], it has been shown that the performance of multi-objective evolutionary algorithms can be greatly enhanced if the regularity in the distribution of Pareto-optimal solutions is used. This paper suggests a new hybrid multi-objective evolutionary algorithm by introducing a convergence based criterion to determine when the modelbased method and when the genetics-based method should be used to generate offspring in each generation. The basic idea is that the genetics-based method, i.e., crossover and mutation, should be used when the population is far away from the Pareto front and no obvious regularity in population distribution can be observed. When the population moves towards the Pareto front, the distribution of the individuals will show increasing regularity and in this case, the model-based method should be used to generate offspring. The proposed hybrid method is verified on widely used test problems and our simulation results show that the method is effective in achieving Pareto-optimal solutions compared to two state-of-the-art evolutionary multiobjective algorithms: NSGA-II and SPEA2, and our pervious method in [1]. © 2006 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
---|---|
Divisions : | Surrey research (other units) |
Authors : | Zhou, A, Zhang, Q, Tsang, E, Jin, Y and Sendhoff, B |
Date : | 2006 |
DOI : | 10.1109/CEC.2006.1688406 |
Depositing User : | Symplectic Elements |
Date Deposited : | 28 Mar 2017 14:42 |
Last Modified : | 23 Jan 2020 12:47 |
URI: | http://epubs.surrey.ac.uk/id/eprint/532836 |
Actions (login required)
![]() |
View Item |
Downloads
Downloads per month over past year