Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

<table class="algorithm-group"><tr><td><table class="algorithm" id="alg1"><tr><td> </td><td>Initialize and evaluate solution archive</td></tr><tr><td> </td><td> <b>Begin</b></td></tr><tr><td> </td><td>  <b>Repeat</b></td></tr><tr><td> </td><td>   <b>For</b> each ant</td></tr><tr><td> </td><td>   Select guiding solution</td></tr><tr><td> </td><td>   Generate a new solution</td></tr><tr><td> </td><td>   <b>End</b></td></tr><tr><td> </td><td>   Update archive</td></tr><tr><td> </td><td>   Optionally apply local search</td></tr><tr><td> </td><td>   Optionally Expand archive</td></tr><tr><td> </td><td>   Optionally Restart archive</td></tr><tr><td> </td><td>  <b>Until</b> termination criteria are satisfied</td></tr><tr><td> </td><td> <b>End</b></td></tr></table></td></tr></table>

<div> Ant colony optimization (ACO).</div>

Computational Intelligence and Neuroscience

alg1

Algorithm 1

Algorithm 1: Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach 