Abstract
Several problems in the engineering domain are multi-objective in nature. The solution to multi-objective optimization is a set of solutions rather than a single point solution. Such a set of non-dominated solutions are called Pareto optimal solutions or non-inferior solutions. In this paper, a new algorithm, Elitist-Multi-objective Differential Evolution (E-MODE) is proposed. The proposed algorithm is applied successfully on several test functions, and the results are discussed extensively. Results obtained from the proposed algorithm are compared with those obtained using Multi-objective Differential Evolution (MODE) algorithm. E-MODE is found to give better solutions in terms of wide range of solutions, spread, and diversity of Pareto front than those obtained using MODE.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007 |
| Pages | 441-456 |
| Number of pages | 16 |
| Publication status | Published - 2007 |
| Event | 3rd Indian International Conference on Artificial Intelligence, IICAI 2007 - Pune, India Duration: Dec 17 2007 → Dec 19 2007 |
Other
| Other | 3rd Indian International Conference on Artificial Intelligence, IICAI 2007 |
|---|---|
| Country | India |
| City | Pune |
| Period | 12/17/07 → 12/19/07 |
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Keywords
- Elitist-Multi-objective Differential Evolution (E-MODE)
- Evolutionary algorithms
- Evolutionary multi-objective optimization (EMO)
- Multi-objective Differential Evolution (MODE)
- Multi-objective optimization (MOO)
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Elitist-multi-objective differential evolution (E-MODE) algorithm for multi-objective optimization. / Babu, B. V.; Gujarathi, Ashish M.
Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007. 2007. p. 441-456.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Elitist-multi-objective differential evolution (E-MODE) algorithm for multi-objective optimization
AU - Babu, B. V.
AU - Gujarathi, Ashish M.
PY - 2007
Y1 - 2007
N2 - Several problems in the engineering domain are multi-objective in nature. The solution to multi-objective optimization is a set of solutions rather than a single point solution. Such a set of non-dominated solutions are called Pareto optimal solutions or non-inferior solutions. In this paper, a new algorithm, Elitist-Multi-objective Differential Evolution (E-MODE) is proposed. The proposed algorithm is applied successfully on several test functions, and the results are discussed extensively. Results obtained from the proposed algorithm are compared with those obtained using Multi-objective Differential Evolution (MODE) algorithm. E-MODE is found to give better solutions in terms of wide range of solutions, spread, and diversity of Pareto front than those obtained using MODE.
AB - Several problems in the engineering domain are multi-objective in nature. The solution to multi-objective optimization is a set of solutions rather than a single point solution. Such a set of non-dominated solutions are called Pareto optimal solutions or non-inferior solutions. In this paper, a new algorithm, Elitist-Multi-objective Differential Evolution (E-MODE) is proposed. The proposed algorithm is applied successfully on several test functions, and the results are discussed extensively. Results obtained from the proposed algorithm are compared with those obtained using Multi-objective Differential Evolution (MODE) algorithm. E-MODE is found to give better solutions in terms of wide range of solutions, spread, and diversity of Pareto front than those obtained using MODE.
KW - Elitist-Multi-objective Differential Evolution (E-MODE)
KW - Evolutionary algorithms
KW - Evolutionary multi-objective optimization (EMO)
KW - Multi-objective Differential Evolution (MODE)
KW - Multi-objective optimization (MOO)
UR - http://www.scopus.com/inward/record.url?scp=76549127791&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=76549127791&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9780972741224
SP - 441
EP - 456
BT - Proceedings of the 3rd Indian International Conference on Artificial Intelligence, IICAI 2007
ER -