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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chengyi Sun Wanzhen Wang Gao, X.Z. |
| Copyright Year | 2005 |
| Description | Author affiliation: Artificial Intelligence Inst., Beijing City Univ., China (Chengyi Sun; Wanzhen Wang) |
| Abstract | This paper proposes a new multi-objective optimization algorithm - scored Pareto mind evolutionary computation (SP-MEC), which introduces the theory of Pareto into mind evolutionary computation (MEC) for the multi-objective optimization. In our SP-MEC, the selection of individuals is based on their scores that include the Pareto dominance and density information among the individuals. The SP-MEC is compared with the VEGA, NSGA, SPEA, and Pareto-MEC on the basis of four different test problems: convexity, non-convexity, discreteness, as well as non-uniformity. Especially, both the Pareto-MEC and SPEA have shown promising performances in solving various optimization problems. On the test problems, SP-MEC outperforms all the four reference algorithms concerning three measures: the distance from trade-off front to Pareto-optimal front, the uniformity of solutions, and the spread of solutions. Impersonal termination criterion is used in SP-MEC and Pareto-MEC instead of the preset number of generations in other algorithms. SP-MEC has a higher computational efficiency than the VEGA, NSGA, and SPEA. Compared with another our algorithm, Pareto-MEC, the computational efficiency of SP-MEC is a little lower. However, the solution quality of SP-MEC is higher than that of the Pareto-MEC. Therefore, it can be concluded the SP-MEC is a powerful algorithm for multi-objective optimization. |
| Starting Page | 105 |
| Ending Page | 110 |
| File Size | 513213 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780389425 |
| DOI | 10.1109/SMCIA.2005.1466956 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-28 |
| Publisher Place | Finland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Evolutionary computation Pareto optimization Testing Sun Artificial intelligence Computational efficiency Power electronics Humans |
| Content Type | Text |
| Resource Type | Article |
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