A global optimization algorithm based on elitism and gravitational evolution strategies is proposed, namely Elitism and Gravitational Evolution based CoEvolutionary Algorithm (EGCoEA). The search agents are divided into two subpopulations with the subpopulation of elites and the common subpopulation, and they updated via three methods. The values of Gravitational Measurement (GM) are used to define the relationships of the elites and the common individuals. The experimental study is carried out to test EGCoEA, compared with Maximal Gravitation Optimization Algorithm (MGOA) and M-Elite Coevolutionary Algorithm (MECA) by a series of typical benchmark functions, including both low-dimensional and high-dimensional problems. The results show EGCoEA performs better than the other two algorithms in solving these problems.
|Number of pages||10|
|Journal||Journal of Computational Information Systems|
|Publication status||Published - Apr 2012|
Bibliographical noteThis research was supported by the Natural Science Foundation of China under Grant No. 60832003, the Natural Science Foundation of Zhejiang Province under Grant No.Y1100076, K.C. Wong Magna Fund in Ningbo University, and the Scientific Research Foundation of Graduate School of Ningbo University.
- Coevolutionary algorithm
- Elitist strategy
- Gravitational measurement
- Gravitational optimization