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Fig. 4.11.
Obtained Pareto solutions in the objective space.
Table 4.4.
Selected Pareto solutions for two-objective optimization.
No.
C
Mn
Cr
Mo
Temp
Cv
Cost$
1
0.1203
0.3498
0.0500
0.0102
691
126
14.5
2
0.1202
1.4231
0.0501
0.0979
730
183
38.8
3
0.1200
0.3679
0.0500
0.1908
728m
157
24.6
4
0.1200
0.8445
0.0500
0.1310
730
168
30.1
5
0.1201
1.7200
0.0500
0.2303
709
197
50.8
Figure 4.11 displays the optimization result in objective space using
the proposed AEPSO algorithm. It indicates that the two objectives are
in conflict, as any improvement in one objective causes deterioration in
the other. Table 4.4 displays different solutions selected from the Pareto
solutions. It can be seen that the algorithm converged to an optimal solution
front that provided optional solutions with different production costs while
meeting the pre-defined toughness requirement.
4.6.2.
Optimal alloy design with both tensile strength and
impact toughness
This experiment aims at finding the optimal chemical compositions and
heat-treatment process parameters to obtain the required tensile strength
