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Details for article 8 of 9 found articles
| Title: |
PARETO OPTIMIZATION USING THE STRUGGLE GENETIC CROWDING ALGORITHM |
| Author: |
Andersson, Johan Wallace, David |
| Appeared in: |
Engineering optimization |
| Paging: | Volume 34 (2002) nr. 6 pages 623-643 |
| Year: |
2002 |
| Contents: |
Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the struggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal search spaces. Other published multi-objective genetic algorithms are less robust in locating both global and local Pareto frontiers in a single optimization. For example, in a multi-modal test problem a previously published non-dominated sorting GA (NSGA) located the global Pareto frontier in 41% of the optimizations, while the proposed method located both global and local frontiers in all test runs. Additionally, the algorithm requires little problem specific tuning of parameters. |
| Publisher: |
Taylor & Francis |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 8 of 9 found articles
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