Abstract
To solve dynamic multi-optimization problems, optimization
algorithms are required to converge quickly in response
to changes in the environment without reducing the
diversity of the found solutions. Most Multi-Objective Evolutionary
Algorithms (MOEAs) are designed to solve static multiobjective
optimization problems where the environment does
not change dynamically. For that reason, the requirement for
convergence in static optimization problems is not as timecritical
as for dynamic optimization problems. Most MOEAs use
generic variables and operators that scale to static multi-objective
optimization problems. Problems emerge when the algorithms can
not converge fast enough, due to scalability issues introduced by
using too generic operators. This paper presents an evolutionary
algorithm CONTROLEUM-GA that uses domain specific variables
and operators to solve a real dynamic greenhouse climate
control problem. The domain specific operators only encode
existing knowledge about the environment. A comprehensive
comparative study is provided to evaluate the results of applying
the CONTROLEUM-GA compared to NSGAII, e-NSGAII and e-
MOEA. Experimental results demonstrate clear improvements in
convergence time without compromising the quality of the found
solutions compared to other state-of-art algorithms.
algorithms are required to converge quickly in response
to changes in the environment without reducing the
diversity of the found solutions. Most Multi-Objective Evolutionary
Algorithms (MOEAs) are designed to solve static multiobjective
optimization problems where the environment does
not change dynamically. For that reason, the requirement for
convergence in static optimization problems is not as timecritical
as for dynamic optimization problems. Most MOEAs use
generic variables and operators that scale to static multi-objective
optimization problems. Problems emerge when the algorithms can
not converge fast enough, due to scalability issues introduced by
using too generic operators. This paper presents an evolutionary
algorithm CONTROLEUM-GA that uses domain specific variables
and operators to solve a real dynamic greenhouse climate
control problem. The domain specific operators only encode
existing knowledge about the environment. A comprehensive
comparative study is provided to evaluate the results of applying
the CONTROLEUM-GA compared to NSGAII, e-NSGAII and e-
MOEA. Experimental results demonstrate clear improvements in
convergence time without compromising the quality of the found
solutions compared to other state-of-art algorithms.
| Original language | English |
|---|---|
| Title of host publication | Proceedings for IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE’15) |
| Publisher | IEEE Press |
| Publication date | 8. Dec 2015 |
| Pages | 877-884 |
| ISBN (Print) | 978-1-4799-7560-0 |
| ISBN (Electronic) | 9781479975600 |
| DOIs | |
| Publication status | Published - 8. Dec 2015 |
| Event | IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments - Cape Town, South Africa Duration: 8. Dec 2015 → 10. Dec 2015 |
Conference
| Conference | IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments |
|---|---|
| Country/Territory | South Africa |
| City | Cape Town |
| Period | 08/12/2015 → 10/12/2015 |