Bi-Objective Optimization Method and Application of Mechanism Design Based on Pigs' Payoff Game Behavior

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td align="left" rowspan="2">Impact factors</td><td align="center" colspan="2">Strategy subsets</td></tr><tr class="thead"><td align="center"><span style="width: 16.225px;"><svg height="16.112499" id="M189" style="vertical-align:-3.3907pt" version="1.1" viewbox="0 0 16.225 16.112499" width="16.225" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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q23 -97 60 -143q57 -72 144 -72q58 0 92 30.5t34 82.5q0 47 -57 88q-30 21 -122 65q-91 44 -131 91.5t-40 118.5q0 95 58.5 148t160.5 53q54 0 117 -23q27 -9 42 -9q14 0 20.5 6.5t11.5 26.5h30z" id="x1D412"></path></g>
<g transform="matrix(.012,-0,0,-.012,9.512,15.9)"><path d="M483 97q-42 -50 -88.5 -79.5t-68.5 -29.5q-37 0 -17 93l22 102h-2q-54 -79 -144 -149q-59 -46 -100 -46q-24 0 -43 29t-19 86q0 78 34.5 153t94.5 120q41 31 94 51.5t98 20.5q29 0 72 -9q26 -6 39 -6l2 -4q-30 -117 -67 -323q-8 -41 2 -41q16 0 79 58zM374 387
q-32 15 -73 15q-52 0 -83 -23q-48 -36 -78 -108.5t-30 -152.5q0 -33 8.5 -50.5t20.5 -17.5q31 0 107 79t99 132q15 40 29 126z" id="x1D44E"></path></g>
</svg></span></td><td align="center"><span style="width: 15.775px;"><svg height="16.112499" id="M190" style="vertical-align:-3.3907pt" version="1.1" viewbox="0 0 15.775 16.112499" width="15.775" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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q23 -97 60 -143q57 -72 144 -72q58 0 92 30.5t34 82.5q0 47 -57 88q-30 21 -122 65q-91 44 -131 91.5t-40 118.5q0 95 58.5 148t160.5 53q54 0 117 -23q27 -9 42 -9q14 0 20.5 6.5t11.5 26.5h30z" id="x1D412"></path></g>
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</svg></span></td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td align="left">Objection functions</td><td align="center"> </td><td align="center"> </td></tr><tr><td align="left"> <svg height="15.25" id="M191" style="vertical-align:-3.27605pt" version="1.1" viewbox="0 0 17.0375 15.25" width="17.0375" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><path d="M599 650l-18 -160l-30 -2q-2 70 -11 94q-5 16 -26 24t-75 8h-94q-27 0 -33.5 -6.5t-12.5 -32.5l-43 -226h108q52 0 72.5 5t31 18t28.5 52h28l-40 -200h-28q-1 42 -6 56.5t-24.5 21t-70.5 6.5h-108l-32 -177q-13 -65 1 -80.5t88 -22.5l-8 -28h-279l6 28q61 5 77.5 20.5
t29.5 81.5l72 387l7.5 40.5t1 26.5t-4 18t-15.5 10t-24 6t-37 4l8 28h461z" id="x1D439"></path></g>
<g transform="matrix(.012,-0,0,-.012,10.637,15.187)"><path d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" id="x31"></path></g>
</svg></td><td align="center">90.695</td><td align="center">17.700</td></tr><tr><td align="left"> <svg height="15.25" id="M192" style="vertical-align:-3.27605pt" version="1.1" viewbox="0 0 17.0375 15.25" width="17.0375" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,11.112)"><path d="M599 650l-18 -160l-30 -2q-2 70 -11 94q-5 16 -26 24t-75 8h-94q-27 0 -33.5 -6.5t-12.5 -32.5l-43 -226h108q52 0 72.5 5t31 18t28.5 52h28l-40 -200h-28q-1 42 -6 56.5t-24.5 21t-70.5 6.5h-108l-32 -177q-13 -65 1 -80.5t88 -22.5l-8 -28h-279l6 28q61 5 77.5 20.5
t29.5 81.5l72 387l7.5 40.5t1 26.5t-4 18t-15.5 10t-24 6t-37 4l8 28h461z" id="x1D439"></path></g>
<g transform="matrix(.012,-0,0,-.012,10.637,15.187)"><path d="M412 140l28 -9q0 -2 -35 -131h-373v23q112 112 161 170q59 70 92 127t33 115q0 63 -31 98t-86 35q-75 0 -137 -93l-22 20l57 81q55 59 135 59q69 0 118.5 -46.5t49.5 -122.5q0 -62 -29.5 -114t-102.5 -130l-141 -149h186q42 0 58.5 10.5t38.5 56.5z" id="x32"></path></g>
</svg></td><td align="center">15.555</td><td align="center">3.657</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

The impact factors of strategy subsets to objection functions.

Mathematical Problems in Engineering

tab3

Table 3

Table 3: Bi-Objective Optimization Method and Application of Mechanism Design Based on Pigs' Payoff Game Behavior 