Gravitational Co-evolution and Opposition-based Optimization Algorithm

DOI

10.1080/18756891.2013.805590How to use a DOI?

Keywords

Gravitation, Evolution algorithm, Co-evolution, Opposition-based, Optimization

Abstract

In this paper, a Gravitational Co-evolution and Opposition-based Optimization (GCOO) algorithm is proposed for solving unconstrained optimization problems. Firstly, under the framework of gravitation based co-evolution, individuals of the population are divided into two subpopulations according to their fitness values (objective function values), i.e., the elitist subpopulation and the common subpopulation, and then three types of gravitation-based update methods are implemented. With the cooperation of opposition-based operation, the proposed algorithm conducts the optimizing process collaboratively. Three benchmark algorithms and fifteen typical benchmark functions are utilized to evaluate the performance of GCOO, where the substantial experimental data shows that the proposed algorithm has better performance with regards to effectiveness and robustness in solving unconstrained optimization problems.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yang Lou
AU  - Junli Li
AU  - Yuhui Shi
AU  - Linpeng Jin
PY  - 2013
DA  - 2013/09/01
TI  - Gravitational Co-evolution and Opposition-based Optimization Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 849
EP  - 861
VL  - 6
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.805590
DO  - 10.1080/18756891.2013.805590
ID  - Lou2013
ER  -