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 |
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