Abstract
Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009 |
| Pages | 933-948 |
| Number of pages | 16 |
| Publication status | Published - 2009 |
| Event | 4th Indian International Conference on Artificial Intelligence, IICAI 2009 - Tumkur, India Duration: Dec 16 2009 → Dec 18 2009 |
Other
| Other | 4th Indian International Conference on Artificial Intelligence, IICAI 2009 |
|---|---|
| Country | India |
| City | Tumkur |
| Period | 12/16/09 → 12/18/09 |
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Keywords
- Differential evolution
- Evolutionary algorithms
- Multi-objective optimization
- Optimization
- Trigonometric mutation
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization. / Gujarathi, Ashish M.; Babu, B. V.
Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009. 2009. p. 933-948.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization
AU - Gujarathi, Ashish M.
AU - Babu, B. V.
PY - 2009
Y1 - 2009
N2 - Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).
AB - Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).
KW - Differential evolution
KW - Evolutionary algorithms
KW - Multi-objective optimization
KW - Optimization
KW - Trigonometric mutation
UR - http://www.scopus.com/inward/record.url?scp=84861862636&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861862636&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780972741279
SP - 933
EP - 948
BT - Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
ER -