An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester

<table class="fixed-width table-group" id="tab12"><tr><td><table class="table"><colgroup><col style="width:8.28em"/><col style="width:2.83em"/><col style="width:2.48em"/><col style="width:4.27em"/></colgroup><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Pair </td><td class="align_center">R-</td><td class="align_center">R+</td><td class="align_center">p-value</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">MOGA-NSGA-II</td><td class="align_center">-0.2</td><td class="align_center">0</td><td class="align_center">0.006</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The results of Wilcoxon’s rank signed test.</div>

Mathematical Problems in Engineering

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Table 12

Table 12: An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester 

Table 12 | An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester 