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
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
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.
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U2 - 10.1109/NABIC.2010.5716333
DO - 10.1109/NABIC.2010.5716333
M3 - Conference contribution
AN - SCOPUS:79952751980
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
T2 - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
Y2 - 15 December 2010 through 17 December 2010
ER -