International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 1, No. 3, 29 Sep. 2011
Cover page and Table of Contents: PDF (size: 238KB)
An Improved Multi-objective Evolutionary Algorithm with the Hybrid Strategies
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Author(s)
Gao Guibing
1,*
Huang Gang
1
Zhang Guojun
1
Index Terms
Evolutionary algorithm, multi-objective optimization, hybrid, adaptive, non-dominated solution
Abstract
An improved multi-objective evolutionary algorithm with the hybrid strategies is presented in this paper for multi-objective optimization problems. The evolution process is divided into initial exploration stage, the middle feedback stage and the accelerating convergence stage by the amount of non-dominated individuals in the population. The hybrid strategies and adaptive population structure are employed to improve the behavior of the algorithm at the different stages. The proposed algorithm is validated by 3 benchmark test problems. Compared with three other famous multi-objective algorithms by two quality indicators, the proposed algorithm achieves competitive results.
Cite This Paper
Gao Guibing,Huang Gang,Zhang Guojun,"An Improved Multi-objective Evolutionary Algorithm with the Hybrid Strategies", IJEME, vol.1, no.3, pp.79-86, 2011. DOI: 10.5815/ijeme.2011.03.12
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