Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization

<table class="table-group" id="tab12"><tr><td><table class="table"><tr><td class="thead-hr" colspan="7"><hr/></td></tr><tr class="thead"><td class="align_center" colspan="2">Runoff calculation relative error interval (%)</td><td class="align_center">[0, 20]</td><td class="align_center">(20, 30]</td><td class="align_center">(30, 40]</td><td class="align_center">(40, 50]</td><td class="align_center">(50, 100]</td></tr><tr><td class="thead-hr" colspan="7"><hr/></td></tr><tr><td class="align_left" rowspan="2">Proportion (%)</td><td class="align_center">DDS</td><td class="align_center">30.14</td><td class="align_center">15.07</td><td class="align_center">7.40</td><td class="align_center">10.41</td><td class="align_center">36.99</td></tr><tr><td class="align_center">DDS-POBL</td><td class="align_center">7.39</td><td class="align_center">77.53</td><td class="align_center">3.29</td><td class="align_center">3.84</td><td class="align_center">7.95</td></tr><tr class="table-tr"><td colspan="7"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The proportion of different intervals of runoff calculation relative error.</div>

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Table 12

Table 12: Dynamically Dimensioned Search Embedded with Piecewise Opposition-Based Learning for Global Optimization 