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="tab2"><tr><td><table class="table"><colgroup><col style="width:13.39em"/><col style="width:7.14em"/></colgroup><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Parameters</td><td class="align_center">Range of value</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Population size</td><td class="align_center">20-100</td></tr><tr><td class="align_left">Number of generations</td><td class="align_center">50-150</td></tr><tr><td class="align_left">Crossover probability</td><td class="align_center">0.2-0.9</td></tr><tr><td class="align_left">Mutation probability</td><td class="align_center">0.01-0.02</td></tr><tr><td class="align_left">Maximum number candidates</td><td class="align_center">3</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Controllable parameters for MOGA algorithm.</div>

Mathematical Problems in Engineering

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Table 2

Table 2: 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 2 | 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 