Abstract
We propose Fewer-Fixed-Objective Optimization (F-F-Objective Optimization), a method for improving the capabilities of evolutionary many-objective optimization. The method is evaluated by applying it to a multi-objective 0/1 knapsack problem. Searching performance in many-objective optimization becomes drastically worse as the number of objectives is increased. To address this problem, the proposed method ranks individuals in subsets of s objectives selected from the total m objectives, where s is a fixed number in [1, m]. The final rank of each individual is determined as the aggregation of its mCs ranks. We begin by introducing the F-F-Objective Optimization concept and illustrating its application to a numerical 5- objective optimization problem. Next, we further investigate the proposed method using an 8-objective 0/1 knapsack problem as an example of a typical many-objective optimization problem. Here we apply multi-objective genetic algorithms (GA) with the proposed method for all values of s from 1 to 8. When s = 1, the method is equivalent to the average ranking method or weight-based GA with equal weights, and it is equivalent to conventional evolutionary multi-objective optimization when s = m. The method's performance is evaluated using such metrics as hypervolume and the C Metric. Finally, we discuss the proposed method with regards to its convergence characteristics and the diversity of its Pareto solutions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 |
| Pages | 520-525 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - Dec 1 2010 |
| Event | 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 - Kitakyushu, Japan Duration: Dec 15 2010 → Dec 17 2010 |
Publication series
| Name | Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 |
|---|
Other
| Other | 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 |
|---|---|
| Country | Japan |
| City | Kitakyushu |
| Period | 12/15/10 → 12/17/10 |
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All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
Proposal of F-F-objective optimization for many objectives and its evaluation with a 0/1 knapsack problem. / Inoue, Makoto; Takagi, Hideyuki.
Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010. 2010. p. 520-525 5716333 (Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Proposal of F-F-objective optimization for many objectives and its evaluation with a 0/1 knapsack problem
AU - Inoue, Makoto
AU - Takagi, Hideyuki
PY - 2010/12/1
Y1 - 2010/12/1
N2 - We propose Fewer-Fixed-Objective Optimization (F-F-Objective Optimization), a method for improving the capabilities of evolutionary many-objective optimization. The method is evaluated by applying it to a multi-objective 0/1 knapsack problem. Searching performance in many-objective optimization becomes drastically worse as the number of objectives is increased. To address this problem, the proposed method ranks individuals in subsets of s objectives selected from the total m objectives, where s is a fixed number in [1, m]. The final rank of each individual is determined as the aggregation of its mCs ranks. We begin by introducing the F-F-Objective Optimization concept and illustrating its application to a numerical 5- objective optimization problem. Next, we further investigate the proposed method using an 8-objective 0/1 knapsack problem as an example of a typical many-objective optimization problem. Here we apply multi-objective genetic algorithms (GA) with the proposed method for all values of s from 1 to 8. When s = 1, the method is equivalent to the average ranking method or weight-based GA with equal weights, and it is equivalent to conventional evolutionary multi-objective optimization when s = m. The method's performance is evaluated using such metrics as hypervolume and the C Metric. Finally, we discuss the proposed method with regards to its convergence characteristics and the diversity of its Pareto solutions.
AB - We propose Fewer-Fixed-Objective Optimization (F-F-Objective Optimization), a method for improving the capabilities of evolutionary many-objective optimization. The method is evaluated by applying it to a multi-objective 0/1 knapsack problem. Searching performance in many-objective optimization becomes drastically worse as the number of objectives is increased. To address this problem, the proposed method ranks individuals in subsets of s objectives selected from the total m objectives, where s is a fixed number in [1, m]. The final rank of each individual is determined as the aggregation of its mCs ranks. We begin by introducing the F-F-Objective Optimization concept and illustrating its application to a numerical 5- objective optimization problem. Next, we further investigate the proposed method using an 8-objective 0/1 knapsack problem as an example of a typical many-objective optimization problem. Here we apply multi-objective genetic algorithms (GA) with the proposed method for all values of s from 1 to 8. When s = 1, the method is equivalent to the average ranking method or weight-based GA with equal weights, and it is equivalent to conventional evolutionary multi-objective optimization when s = m. The method's performance is evaluated using such metrics as hypervolume and the C Metric. Finally, we discuss the proposed method with regards to its convergence characteristics and the diversity of its Pareto solutions.
UR - http://www.scopus.com/inward/record.url?scp=79952751980&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952751980&partnerID=8YFLogxK
U2 - 10.1109/NABIC.2010.5716333
DO - 10.1109/NABIC.2010.5716333
M3 - Conference contribution
SN - 9781424473762
T3 - Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
SP - 520
EP - 525
BT - Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
ER -