پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی
چند تعریف
بهینه سازی روندی است برای یافتن و مقایسه کردن راه حلهای ممکن تا وقتی که پاسخ بهتری پیدا نشود.
پاسخ خوب یا بد با توجه به هدفی یا اهدافی مشخص تعیین می شود.
بهینه سازی چند هدفه و تک هدفه
بهینه سازی مقید و غیر مقید
صورت مساله
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rLdCoVFTODeil/MFW+K0vu+8w0HT4b0jy38f6qfH1fjDUWOCbp2lYq5WsslEcdpkNN3WrSjjQi6pgmx63FYuH8pJU6TqWlpaXpXyr9bUb3TG8iBalvV+9UqpwZ1LNaral9MNX8WHU6nbpPo9Y46JBKi4uLFXoapKuc0kyH4cPuHA6H3Euf8CYPaXBrVnV6R1OYEyodKus2/XakEVH30j9upZvzdLzk3cjUcSpNZ4J0eRPKfbEabCIlqW9X71SqnBlUku5xT20eiYQ/VqWpXTQfjRTzmaWNXp2mUmkYinJXfLynv8gS/tqTn/unadkbnkx8Taiv2zTbkUaELtI8buW7x1J+zIS2GsGZrM5SqcPhiBiLlorfZnSPf+Vem00kL93t6pdK1WQGNaQhU0LUVKAqPyDSj1Vp3HDEj1X5aW2an63CH22a5mNOG5a6S6VWq1U6LBL2IsQbSZrwBnOXyyUPamlAfWyZ+D0knVw1PIhTqNvU2rGBNqJ62fnztwFJ7bi12WzyeIt60lHaOM5kdZNKtRlQsmx41KoPdpzqNWZFg+3qlErVZwZl4XNUhy9X/wGJN/FTuEw8mzc7TxYZT6Uul0s+ttR/RcpTcEsSfq4iZkVpQLd3ZOL3kDxTktFoTL/PJp26TaodG24jqpe1P38bFpXHrcvlkqaAjZh1te4LHE8jOJPFTACpr04aTp6JJy6FPdE0XH2fRl+NWKlU43YJk1pmiLcq+ZwSMQ2WRP0HRGHWOV36vxuxzKZSabSKPO9JJkauREwuo+8kJqnR9veQhl8WdVa3jaAR1cvOn7+NkvSAXJPJJE/josvTj6DKwZk3tc+LUetuPKH0kF3L/N5omBmkVUmfTU1u+pSGi4WHUelhrWmuFhHq9fQlnL8bB9oRDRHHLQDUsfqbSrnW2TjQjmiIOG4BoO7V31Qq0lfRWNCOaIg4bgGgjtXrVAoAAIAsQSoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgCQpVwul91u17sUwH6kUgAAsoXVai0uLhYEQRAEo9FotVr1LhFwEKkUAAAA+iOVAgCQcTabzWKx6F2Kg2w2m95FACKRSgEAyJTS0lKz2SxdMXc4HHoXZ7/S0lKTyaR3KYBIpFIAADTmdDotFovBYBAOqFcp0GAw1KvyABJSKQAAmrHZbPJwIkEQDAZDSUlJaWmp3uU6hMlkIpWiHiKVAgCgDaPRKA9vt1gsTqdT7xLFZrFYSKWoh0ilAABooLS0tLi42Gaz1dswKpPmh9K7FEAkUikAAKkzmUxSDJVuJG0QY9tLS0sFgQCAeoeDEgCQ1axWqyAIqc0nL42vl/odwy/f15/h9jE5HI4GkUrlxJ/yu1Jbg17q+WFTBxrAQQkAQObII5PivcButyvEBTn3OBwOk8kkr62kpCSpYiSczdTlcplMJpfLldRqY2oQqTQ88af2rtTWoCNu9q3vByUAABmVsK/UaDSqTzYOh0Meg6/+XTabTXq9QseelLEMBkP6nX8ul0uTVJps8k52JZnoK61XzzKIEK9RNKnnBoFUCgCAErvdLgiC3W5X/xaLxSIFU5U3BpSUlMgzScXbkNwXqz6YSsWI2QOXfiqV0nyak15pshL1pPSvZnN2u91gMKTwG8DhcMjPTTCbzSrfZbfbpcaN/lMdV5G+SKUAACRgsViMRmNSb5GyrMFgUPPikpKS8BsAFBKwFDSje2Gju9OkNBMvHqWfSqUu4TS78dJfSVL3Ykp1ErG56K1Lo8Hk3wkqVy7dYiEcKmEwlfKoNI9YzEbRpJ4bClIpAACJGY3G6LBotVoVrghLPXMJ1xx+36r0FuUELOXd8O1K7wpfIl2jDxdxQ6r6VKqQh4xGo8qwntpK1ESxZONacXFxeNCMrjpRFMMfyqWym7y0tDTiXQnvVxZF0Ww2h6883ovV13NDRyoFACAxh8MRkQxcLpcURBQu8qq5tTSiO00KncpvkXpM5f/KXXRy9LTZbFLScjgcUiEjpqxSmUql0BYv+akpasoriZkXoxkMhqRuFS0tLQ1vx5hVV1JSIv1XKkN0f6dUZvkt0n9NJpPdbpcPBpfLVVJSopAmLRaLzWYL7+uNd6VeZT03AlmxkwAAqFdcXBzeg+V0OuXb/uRoJc2ZH+9mUIfDIb1FzX2l4W+XthWeZc1mc0TqlV4THnfkfCwXz2QyyXFHGnEfEa1Uphw5tMUrhprYndpKovNiTNJrpOcXxLya73A4HA6H1WqVMmJE32fMqguPhtINpuGvl2+NKC4ulv4bXtvyFqV0qzyKLrpj1WAwRART9fXcCJBKAQBZLXoMfszrsGazOfquQYPBEH77ZvRf1RQg+o3h/ZrxVh6dXQwGgxxVo18f0WknqO57k8fuqCmGhiuJzosxyb8NVIqOdwmrLmXKB0DEFf94NwBIGO0EAEDjJ5/4o5fIpMgijUeJzgrxgpHKYfsRfWYRUSbmmmMGtfDbWGO+K2K16qsoOo5L1I8xT20lEXlR4TUx1xzBaDSWlJTEvN0iYdVFsNls8XZHYjAY5NsAFEj9rNJbSktL5akY1FdRI0MqBQBktei+UnleJ4PBEDEeRbqeKwhCcXFx+EXb8DxRXFxssVjUjw13Op1SMDUajdG3SEoPMpVzldlsVnioqZxopUv8EeEm/JVJXRGWxk6Fr9BkMiX7bNXUVqJmxJjL5bJYLNExsbi4WBreHn67ZzzhVacQc41Go3yoWCyWiB8kUgMlNYlYBPk2g5TruUEjlQIA0BhIc0vF/JN0NTzlNWsy637KK0mn5CopV136+w6VqGgAABoDacRPzD9JV4pTXrPVak1/tE1qK1HIixpSqDqpQzTTBYCEVAoAQMPmcrkipuEMJ43BT/lCsHQ3bZoP6oxYifJDQcMp5EVNKFedNCWTygd0IX2kUgAAGjCr1SrdBxkzPMlPzkxt5fJYoojbZJN6Qn3ESqTx+FIHZPi/IyjnRU0oV518r3DCQUuZk1Q9NwKkUgAAGh5ppkx5XE50sHO5XPJ8TKl1N8rPK4q4hq4QJVWuJGFfqXJeTFPCqrPZbPJfdewoTaqeGwdSKQAADYx0n6g8ADwiNUbMMZTCI9Sl5xJJbzcYDNGpUU0fXsKVxHyLcl5MX7yqc7lcdru9pKQkfAC+7k+fp68UAAA0SHa73Ww2hz90KoWuPim3SfMTRT9qKHMrUY7amSY9o1Uuc/q30iIFpFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5VqotYfDIUUXxHx11AwmMHiAAAANDSk0vRVOjfO7D1r89/V0n9DwWDgUBW7N87sPWvTXxXBYLA2JIqh6vKt8+7+1LXHW6flrA0GAoFAsLZONwoAAKAKqTQ9Nfvsk3pcOGiGdVO1PyiKYshdYbmx61mnHuLkE49rLhQee+LJZ3Q+f/IvLr8YDFT+svjB83t0nfBjWaBOyumv3Dj5/HNPP/XUU8/qMvjDCk+dbFQfoVCotqr8wyHnnnH6WedO/sVVo3eBAACAGqTSNLjLl95yUefhC1f9WC4vCwb+3vDdmm8Psfpj87Wti04ZY1lh+257dUC6mB9wfL5yfM/zL5j0U3mGg2mgauOkrhecfd64Fau//fbz5Y+ceUbPwUtc7sxuVCchd4Xlpm5nn3LykYIgCELTEd/v9eldJgAAoAapNGXVrg/v6Hx+8bKf/YlfG3L+snFbddTifbZXBx3f/Zal5RmMiP6qjc+c/Y+c4x/5ere0wPnd4ycWnX7+MxsrVRQ8g2oqNj53ftduXQZ/4KrW7q6CYMBp//Zr68evXJ/bLCe3cPQP+0ilAAA0CKTSFHkqPrj93COvW7RDeZBTIhW/FJ/a4f9uWlrpSX09ftfm2f2Ny/6O1eca8lR+OPDo3MLTn9slZ9DA7y+cnt+s0zVLK9zpFT493n1rbhcEQRi4cm8aOx9HqPLlMwsK8/NHriWVAgDQMJBKU+Ku+PDWzh1OffSnstr0AlWtd9dzp+UX/etGS1UKETHg+eimrt3PO3u85eM1f/n8MVbgqfiof2Fe87xbbRXy0Cpv5Zo78pvnFfSzuPSMpaGgx7F23brvd3oC2ofSKtebXQuaF5BKAQBoMEilqXCXfXhlhzb/fmabO+35nYIex6TTWuQ3Ty4i1no/uaX7+V26DH7xk6XLP/10c5zkFfK4Pr6yZbPc/NPm7a6Qi1pb6Xzx9ILcpi2u+Khc197SzAlVlb3WhVQKAEBDkpWpNPD5vZf1OLdz586dO597fq9+8x1JDjfyuD6+okP7VkO/dMUZyx4KrDL27de3z+zt7v3Xzf2OuX0vvfSSPrN+rYi4m9Nbabstv0VhiyuWqbqg7t8ytWfPnucPnvfRov9+tOyr3YovrinfOK5Vs9ycgmHflIfNQuV1fXtnYU5u05YP/1ym2wj14M55l/W+9JJeUzdXaF+GTKRS/yrjVVdfNuXnypAoiv6tM3tddEHPS/vN37r/9tyarTN69ezW89J+87dW6XvDLgAADVO2pdLA7y/2v7TnPw675PnX3l2yZMmS/7wxotMxXS+4sFfPrl269Lh02McV7oQjbzyujw1tiopOnvmnK7qrtKZy8/RLe3buIAiCIPzjlk+9QVEM7Zx50Tn5giAIwnlv76k8dAO1lbvn/asgt2nLqz5R7rkMrLjjoot7dH3s3TcXWyyr/1Szt76ydaPycnNzCh6wh4czX9n6Bwtzc3Ny7/2+znsSayo3T+99cbcuXbud3UYQBOGS9xzRY51C7spld/bucV4C8dtLy1QqFabnueefIAhHHDf19z2V28zXXjXh5TnDOgmCcNSEjZ4an3/1/QOef2v2M32EJod3fMJeTiwFACBpWZVKff7VIy48ThAueP69TXL+21ViEARBuPiZBW8uWrJqe03Cmxz95ZseadWyqOCWL8pjzIJfW1P245Il7/3nnfuPOyynaevrV+6r/PqRETNfe23J8uXLV1q/2emP3IDX9c0dhTm5TVs9urksVprxer64/6orrrh8xBuflLy/5P316lNWqLrs/T6FTY7IiRiK7itbN7owNzenYFTdj1CvrSn7ccni118xdhKEJk1Penj9vli3wwZqdqxasuidBOK3l6Z9pYGaHdb3Xnvu0sNatj31qQ2Vnz04+5P3Pli/N7C4R1GbFsdP+tXpeO2hd1ct/rE8uPKq1m1bHTtph6tuJqEFAKBRyaJU6q/6de7ZhUKTkx5YFzZHU417y/iClq3yL1umcnaiUHXZoktyWzbPH71OMfDsfOvqvDwh75SLLh778jKlnk1f2fqHmuc2OSK39+J9VVERK+Qus/Q7XBAEQTjnUbu6Iso8ZZ/fXNisaU5BZCrVr690v73TOhUW5pzw5A5fRp68qv0VfOeCczu1/sfYBdNHvmtd6xdFsebzGzq1Kzpx/M+b583/fOUuURS91uvbtGjedMjqssb8kAIAADIle1JpqHrfogsPyy/Mv+V/noM3Mgaqts06LTe36WEDPt2rbuRPqKps4TmJA0/IX/Zx/8PzcoU+z38TPVFpOF/Zugda5B5+eEHnV/dVRkfjQM22VR/998MPSt68+4R/XdD9ot4DH7GpKae06h9H5+XmRVzBD7nLPuhXkJOTd9L0Xbr06gWqtk4/Oa953q1nK88AACAASURBVPHFW9yZudatcSr1uzabTm3R8sQbxr/y4fI/RFEUfdWrBrVvU3Dco6/PmbHCXi2KYm3VluJWzVs1uWzpHxpOvwoAQPbIolRaue/FU5s0L8h/eEv4yPldM//ZtKlw1Aib06cuS8ip9P4fFQNPKFi+/pEzj2x37ks7qhT7AxOk0oP2rn731fnTH+l6+Ende/a66sa3nAnLGqzYNfWEnJxm+f0+2FctZ+5Qlev18woK8nNuXF2uR69eoGrrjH82bZ573GO/ZSiUap1K3ZUf9WmWf0TzC5/f5BJFURS9LuvgDnk5x5w+8LH31rjF2v1Ju3nrZn0/2K397KsAAGSD7EmlYqB6w4u9BeGwU6947UCe2/PGtT3bCkLv5z78XYyRB0OeyhX3XjPg6ns+CR8e7ylbNTi3sDD/siVhSS/G5nzbX+7e/PC8ppd+VKH0OtFXtu7+5rnNmuYOXrVPRaCp3fT+Gy9OnzK4RfvOPS/pd13x90ovDlRtevQf+S2an/NGZaW8ar/rt6ePKmiee9zUP2IM18q4QNW2WSc1ad6k0/hf44XSkKdq1egBV/RO4IoBo1dWxuzh1jaVequ/GHJUy7xj7lz0a21QFEUxULF9QtvcHCFv4KwvKsSQuD9p57VudunSvzz0lAIAkIosSqVirVjz46uT+wpC+y69+l522WV9Lu7WvuCGOa++syFeNvP7Nr1nXvDSu794aw5mn6Drj6nHFbZqfs4r5fG7Nv2i+P0n705/9KLctscOWVWulDZ9ZesfLMgvbHas6Y9y9SHx78/+89KcKc/dcOwZl17S76qbnvqfL8bQK1H0e0rHHVtQ1OnG1dXy391VH12W3yq348M/V+owL1Sgatusk3NbNblkXqk/7u0Dft/mxQvmz05gvnnR5vCWOUjTVOqt/uKGdi1bHPfwb/ubO1C9bfZpzZsffsnTX1cd3KmTcls3vWTJX0yPCgBAarIplYqiKIq139zWsd+YJydNnz59+oz5r69MfBk8ir9yw9h/tG7X6emtFXFiVTAkrn3PsrPMu2faie3atineWl0riqL9ne/97ugc6K765MoWrQqPfmhDCiHRuXzhrOeeHNm1+XXLd8csTaBm0+yLjyjocP2XNQcib+jrGzsWCBfN2lijw02lgertc07Jbd2015J9flF07/27NpiJ/tpq11vdNEql3orPB7dr0eLYMZv2N16gevuck3NbN+u1uEyOqdvnnJzbJqfXor/FsjKxVoceaAAAGrxsSqUhX/XXj9857NJzShK/1l+1deGNQ6657uZbjXe+vi1iWnS/e+OY49sW/Xt66SGDhfyVW94e88j0tb4a56Jn37VuFgNe94aH/tG27Tlztrr/Wjz5PeuPnugg6Kv8YuhRRS2OeXBjjMSq1u6VX2z3eGP23NaGKr54speQd/44aZDUvrdu7pJ35LC5X7tDOlxqDlRsnnpqbttWA60un3PpPeb126trM1KMv184Jj8/r2DsFnlJyFv91cR7R4546ssqr9S/GvIlXiIGKrY92a5162Me+PlAKK3YMuvUnNZNL1z05/6L9YGKzVNPzT+yZT/LPtt7X7s8Ku9QBgAA4bIplYpicOGxgiAcfubVg4fdeeddd91196j7LRWxX+ndbZ0++enr2gnCWfNKKyI6v/zVG+4/plVh34+qwkcLeZxf3SQIwvF9+k95+/UtYjAkigH3L2OOFoSW5/eb/NYbW8RAdCdawL3rqXYt848c/VPsUOqv2vbq0JuuHaBg4KDr7hg12eb2xb1NoOa7F+cO6XjW1VcNGGC4pP2Z4+Ys3qmitjLBu9c2VBAE4fje11wz5S3T6r0ejW8iCHmrVo8fem3/C9oKgiAIHXr2v3bo+NVVXlEMVf49o50gCG2mOV1SbAxVJF7iL98w8RihSYdRP1VLJQ1V7Xn7XKGJ0LXkjwN3kB7YqaMve8D86eZaPbqgAQBo+LIpla6dOHzC1Bnz5s6aMOwk4YBWF98wYsy0n8VY424qlw85q92lJY7oMfT+4C9v3HFip35vO7wH/xb07flmwYszX3jeZHUdWBYI7fzAPG/alCmrXJHrkFZUs+ah8/KOueW1XwKxR/4EvX99OuX5ZyYqm/TCK+s9Me+wlP2xdPZzEydOfMY0b61uTxkVxaBvz7evvDx37tTJU6ZaY1dJekI13p9enTL56UlT586bN2/u1ElPT37hlfUevyiKAd/mxQtfeXnRJu+Bifv9iZcEvX+unPfaq0u21+5vn5Dfu+Gd1157+X8u/4Eu0aBvz7evmOfOnTHttc1iiBH4AACkJCtSaaBmz0ePjpswd97kL93SgrULXpoza9asWTNNT9xxvCAUXbRwc3VED1ewatv8/2vZouebMZ6IKYqiKP7y+u0dz3hoTXpXa/98r09Rp6ELf0lrJQAAAA1eFqTSkM/16chzcpsN+Cr2378fcXp+q3E//31oD2KwevtLnVu37vFGvFAqhsTf3hh72UXGSck+cemgH58dctK5Y1/dnPIKAAAAGolsSKXVe5ZcKAjCiTc9tNwd+Ufn2/c8cNU141//2eM95MprsHqH+dzWrS94fYfig3qqrE8N79F1+F0le4JJX7hd+/Q9PS+8e8LnVcm+EQAAoPHJglQqBr17v5j67GO3/VM4tv+QW4cdNPzuYS9MG/PEs6sqo95SvWPBeS1adH1tV8IL9NXWCeMeLh7zzq4q9c/08VXsenfko+PHPvVZ1JYBAACyUjak0v18X5kmPDR61H0H3T92vKU81ksDri0Le3TocteqPX+vNP/krUo4qHrbwk+d1V71qbTyj0+e/iyq4xYAACBrZVEqVS9U5Vx0oSC06nPP8PkLp61TkUoBAACQFlJpDCGf5/vZTz4yZuTI0Yv/1rswAAAA2YBUCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoL7tT6cZZjz42/rmvq72hNFZS+fFDS3aWexVf4/1y8pee/f/2Vf354YzNaWwQAACg8cnKVBqo3lFyr/GOm27p0UwQhJ6L/65ONZWWfzjaOPqx8R/8UeGTFoS81d88c+9dtx3ixuuuPFn49w033jjMOKJkZ0Xl7o+eHD/c+IClTLM9UqF6+ZgRd9x22213PzgnGyLxry8WPzz2kcc+/N0d0LsoAABAjexMpZ7fP3xs/Kh7exc1a96s6xs7KoMprCQk/jL3wRuG3Dpu9k/hS32etbMee/iBQ9w3fNBJgiB0uXPEuMc+/MMfEsXK/44de0m3m8csK9dqnxRVLn98/NAuF48a9cADI++9uGO/R2f9XCfbrXshn9s2eZRx6E3nHiYIgiDc+b89yr3YAACgnsjKVLqf58VTO7Xq8sqO6hRCaa24ft41p/R/8H2Xqpe7v5w15+3fDl22ceadp/97xOyfxHRuH0gsGNz74fBzhYLeUzZIC36ec2XztteMs5QHUwnj2gl4/vh44sSnJ87+zuPTrApCPvd30x95cMTwIWcd3qRZfov7f9jn02rdAAAgk7I2lda6d77a48jW57+8vSqVcGafP6R956d+SK8MTouh3dHXzbantxZlft+6Cece3vy4u9bVHoh+oR/vPiZP6PzkD76aTG45Ee+er68TBEE4552/q7QP5uUzTsgvyM2/by2pFACAhiFbU2mte+fr3Vu372pOIZTWBOzzhxzTwbDkd196vY1+7w93HZ3TYeAceyBT+bDGYx/RLrdl7pUrqtwHlrmrV16V1zKnjfFHt56JLeD5/cMnnp74xMd/uTXvtA1VVbzVraB5Qf5IUikAAA1ElqbSWveuN7u1bH/UhG2upAfD+KrW3XtM0fHD/udJO0rWeL6/s1OLpm3vyVQ+rKn88b6OzfLzj3p2e7m8owHXjskdC/Kbdhi5rkK7a+f1Sqiq7LUupFIAABqS7Eylte7f/3NBYdujhn5xYEanihXPTnhswguz5q6p8NcqvtdfbR95fPvWV37sqlZ41c5Fc+bOmf1tde2BbsCaNXNnz545+6u93vCOQXfVCkN+q8IOI3/KSCwNuLY/26lZTk7+wBUu98H86an4dFBBTk7Tjs9sK9dvhLp/zdxZs2ZO+eRPj/ZlyEgqrbHNmTd3ynKnKIpi6YJnnnj00cefnTV/8S6t1g8AQHbLylRaW+V4s0dB26NutnlrRFH8+dUXJrz4/ODrr+2SIxzWbcGvikPy/ZU/jj6mqO1RT20pixmmAp4/l096fGwvQRAEofvUnwMhURQ9Kx4ffrwgCILQY+kh01AFXNufPaogv2nH++2VMXteQzUe+8uTJz6RwMRJC+wxxgz5yn4cnZebl1Pw4PrwcOYr++mhwtzcnNwR39V5T2LA8+fyyc9OeHLC0w+cKwiCcNRzv7j8kS9Kb69FjVOpVOTHxxnPEg4TTp7269qF0xbMG3vLLYMHXVIkCG0HT7Ee+lMDAACkIitTqbvCOrD5UcfftsYritvfXDB37G3Gt52i6H/9jA7tW4/ZsE/hynzQtfO5Ti1aFxr+W+6OefHbX73t9dtvHXz90B55Qk6zY8eVBkVHydRHBw++1Wg0jhoz+ZtDp+x3VywfUJCT2+zoF3bF7LYMeau/KL79pusSuOm2x76o8kQWKOQpW3FtYbOmOQWjfjg0la4bXZibG7m4Tvirt71++y2DrumcIwiHH3HkZYt3eaITXVp7LYoap1J/9bbX77j5esPJh+UVdBzwxAszn7jn6TWiKIpi+aLLO+YIwhUf74l9MAAAAPWyMZX6q39+8F/Htrnqk8CGN0r+88Tj30lLd793cYt2eb2X7lOaU99TtnxQbquW+fevS5R21j707yZ5TTpe/8TjT4955sd4r/KVrX+oeW6zprnXfrpP82QTcpd92L+w6RE5haMjUun6MXr1le4XeOO0wvymbYZ+l5l5ADJwBf/v/wxolSscffWY8d/Jyzw1S3q0atPqtHl7K5Rv+wAAAAllYSr1uzeNPblt67ON85+c/NCT/5MW1np2v39Rs9bNTpn5a4XCbY6hqrKF56hKO0Hv7y+dLhxxuHDy1a/+Ff9lvrJ1D7TIPfzwgs6v7qvUPNkcvIJvDy+up8x6Y2FOTm77J0rLoq6e14Faj3NJ79zmea1v/Lrak/jlKdA6lfqrNjx8dLNmLXsWrwlbGqzePfukotbNei3am/LTwQAAwH7Zl0r9ntLxJxTlCedc+tDgN8r3T+JZ6939fq9mLY84cdbWKqWxN3Iqvf/HRGkn6P190ZUntisasHyvN35kyWwq9ZeXFrdtltMs/0brvoNXukNVrje6FOTl5lxuKdcjTdV6di+9tEnzvKLBX2UolGqdSmur/pp6XKuidjd8FQi73aDWv29Jn4LWOSdPL1X6JQMAAFTJulTqr9r02PE5gpBz3kNfyAtrPbuX9M4tanf+K9sOPjbd7934rnnBS+/+4q05GOiqy97vm1tYkD941b6YNzQeVFu758vhHXNaNrv2i8r42ctXtu7+5rlNmuReFufOAb9v8+IF82cnMN+8aHNYMWXeis+HtM4pKDj3rcpK+a/Bij9nn1zQPLeo+Fc9ukprPc4Pezdp3rT5DXFuChXT3WutU2mt56/3L2vVrsN1n4W3ZCi09cVLm+YJJ8zcovhLBgAAqJJtqbS2avfMEwrzWnQZ+01I7vaq9fy1pE9huw6DVrsPprSQp3LFvdcMuPqeTyrCbvj0l//6eFFBy4JzXi1X7NoMiuKWz1+48uLjjmh16uMbK+LePOkrWz+mIC+/adHjsRNiyFO1avSAK3oncMWA0SsrY92X6q36/PrWha1OfqzUI6/eU/XVjXktcppfu9KVIFlnQq3X+WHfnBZNTxy+2hPvEfXp7rW2qXT/4dF+kLUivL6q1hiPEYTj+y50ZGBqKwAAsk+WpdKQd/cH/Vu16zDwM9/BJBHy7P7gssJ2HQZafaLfI9YqXkj3uj69tl1R61Nm/6X8UKid6/+79o+qLcUdjmx73NQ/q0OiKP71466gP+JN3qpvbylsUdBq0KcZSohe92d3/rNJ3omP/HZwy1uLT8o7/MQ7VlUr3FqQKbVep6VvTuvcE6ft8Ipirac6pFzfqQlVud7U6tlOtZ6/PujXvMPxN35WGVZff3w8pafQ/PwBL/8VYKATAABayK5UGvLu/vDy5u3aX/NpWK9XbfXvS/q37HjCTV8613y+/q+aYFD07/vu7bdff/v9Dz7+YF1ZzaFB0lu5amCHti37v/97dUQcCfrKNnz62Xd7RLFq7aeLXrGJnoqqlYPaty26yrI3VLXeuujlzz3Vh/aaBqpKnzi5qGWbASsrM9VtGfA65vc55fB2d37okEr54xvDjmzZ7ZoXd3p16OKrrd61uF9Om6KzX/4zULlx6cqNW73BjMz1WfnSaQWF+QUP/XqwkQI1O79Y9smyLxw1gZD6JSGv09Ivt03Rv16Wn5pQ+8vrLw4tPPzsgaO+EhnlBACARrIqlQartr9xgZDTyvBJeMeke8+y/kKTw9sOnfmfaWO/9VWFxFB16ez+fS48tZkgdHhyfeSVdW/Fimvatsof/GVVRC+cx/nVrYLQ9voXpi2e/dC3AXdIFH2Vnw5o00w496Ep78wcawtWR6yq1rN35okt8ptftTxjoVQUxVr/7wsGDDrr2NvMr7z11oIJ3Y7sNWDU6lp94pR3r+1W4TBB6PPM3Dnvzxi4cGuZW+MtBGq2rXjvjflPXiAIgiD0fGr+G++t2FYTEMVQ5Z7ZRwuC0HHm3y4pqoYqVCzx7ll+VbMjBEG44Mn5r7711ltvvWJ+5vITL+p71QNfaVxyAACyW1alUn/FpumGG66/z1odlgFDnqpPRw3o16d338tmbw972qhn0xNd25/2/Ibo2z3d1StGdO948oNr9wYPiXb+qi0vXXPFJRf26jt7m3//X7zeLx+8ul+vS3rPkhcdFApuf+Oa3HbnDV9enfEbPENfP3Blr27dul3Q96qFv+t3zdlfteWlawf07durZ59+s7dloJc05KlcfvdlF3e/oFffvn379u3Vo/vFfYd/UuERRdFXuWLkgEED7l1+oKM85Em4JOR1fjKwRavmp158ycU9unfr1q1bt269Lr/vK67aAwCgtaxKpeqFvM6Pry5qc9IzP8d84pLPu3xk97b9X92aXpZ0/fjg8e3OH/6JV6ep7JFIrfuv/15Z0K7wwiUZmsIKAAAcQCqNJeTdvWxgm3b/nLihPPbUSSGPe+XI3r3uX/T53pQ34rTO7n/8JXcvq443Eh26q3GvGZrXtqDTxJ+ZkRQAgAwjlcYQ8jo/GdSm3YlP/RQnlIqiKNa6f53e+4JuIxd/sCmFh2Y6P186+oILek8vjRwyhfrE/ctjp7drd9r0nQxqAgAg00il0UJe5/Lritoc/+SGBCNxQp7NU3r16nn/62vL/Oq70oI1lXbL1OsvuXTqRp5TWV/V+itLP1/+/sxLBUEQLp+zdMXnpRV+fkAAAJBBpNIoIfefK4a0P6XrtNJ923/eG0iQRUJVvi/ue2lbeZXqgOne++u8/vO3lqXQw4q6UlOxaUqPbmefedY5nTt3Puf/zu56wQsbXbQYAAAZRCqN4v571Y0FrU4b8+6ilTPv/ayK9AgAAJB5pNJIIXeF5eauZ/37jM7nP0/3GAAAQN0glcYV4qZPAACAukIqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFACgJ7vdrncR6pTNZtO7CHrKXHNnecU2DqRSAIBuXC6XwWDQuxR1p7S01GQy6V0K3WSuubO8YhsNUikAQDfFxcWCkEVnIoPBkM3hKXPNneUV22hk0XcBAKC+SS2mmEwmp9OZifJkmslkyubwFN3c4U2ZTrM2lIptuIdu3SCVAgB0Y7FYkk2lZrNZEITi4uIMFSmjLBZLgwhPGRLR3OFNmWazNoiKbdCHbt0glQIAdGOz2bKqr9RqtWZzKIlubq36ShtKxTbcQ7dukEoBALpxOBy631dal0GhtLRU9/3VUeaaO8srttGgCQEASbBarYIgWK1WTdZWl6nU4XBEL6zji6p6pfB60kWXud3X9+dNPaneRoBUCgBIgnBAvBfY7faY+U9hhVqUK7F49x0mGylKS0stFktqZdAlPNWr2xnrPpW6XC6TyeRyuTKxXbGeVW9DRyoFACQhYV+p0WhUOEOXlJRELEk5pkSvSpkmeUiacVMQhHjJW7lULpcrc6lUYdOZ6MyzWq1SVRgMhpKSkpixT31zJ9uaERQqVkqNBoMhqRpIqjzJVm+aO9uIkUoBAFqy2+2CIMR8hI+UaEtLS8MXppbSYq5KoUgmk0mTOChFHEEQYgYLNaXKUCpNqkLSJ83xFM5oNKopUszd16Tw8SrW4XBIra8+mGa0MtWvXJqyoP7PLaAhUikAQGMWiyU6o4gHokxEnkstpcVcVTQpj1osFqfTmX4clDrkSktLi4uLYz6jSE2pMpRKVVaIJqQfHtHUFCnm7mtSeOWKlRKeyuvsGa1MlSuXwqvEbDZnoiT1EKkUAKA9o9EYs7vUaDRGBNaUU1r0qiKYzebwMsTrpYt5k2jM5fKkmArXixOWKvqNSaUfhRcrbFrbgCV1Pcp3cUjj32NuWn1zJ6y3cDF3J+GBJIXp8GZNrTLVFEaMf2ipWbl0gIXL3H2x9QqpFACgPYfDEfO8KyWD8CUx79E0GAwJh/lHryqcxWKx2Wzha46+bCrfJBpxYTfecpPJJK8h3qaVSxX9RmkKT5XDp6QXx4tB8Tad1CbUkBpXKonL5SopKYlXKpXNrVD4aPF2R83bw6fxT60yYxYmeiXxDiGVK7fZbNIOOhwOaT02my1hYRoBUikAIC3FxcXhXZJOp1O+jzPibC39KeIqqnRdVU4q0vVxqX9IyqYOhyO6oyjmqsJJsSmCwWCQY2XEhuRdiLdcPJB7HA6H2WyOebdfwlKJUeFJqiiVnWHyi81mc0TcUdh0UptQL/zWUoPBoLKNIpo7YeGjxdudiIqNV0Xyj6XUKjNeYcJXonAIqVy5yWSSq0iaXiBLLuKTSgEASYgegx8d/qSTqHzCjhDRYRk9bkY9hSEjUg9TeGyKuQYp+KpfLpOihtRNqL5UYlR4kjvVVF5kl4dbqa+QZDcRQf6NoSBe9UYXSaG5VY4uirc7QlSnbMKtpFCZ0eKtRPkQSniQRFB/e0ODRioFACRBPk1GL4k4g0oDjCJEd/lI10DDSRnI5XLJcw/FlLD3yGq1ynGqtLRUIUFGR6V4yyVSHoo56CdhqYSoS7dOp9NgMKiPHfEyosKmk92ELGalqaemuVXWW8LdEVSk0uhcnkJlRou5EoVDSM1BEk19eRqurNhJAIBWovtKpXv1BEEwGAwRA4zCc5vJZIp3b5zVapUvuEu5Ify+Uum8Lm9FeVXKSkpK5A1FXEcuKSmREnC85RKj0SgXLKKTTGWpYl66jX5AvAJpKEz4LQpqNq28iZhdd9KvApPJFBGtjEajyWSyWq3Su+x2u9lsjniNyuZOuTWjdyeiYi0Wi9xwRqPRbDbH3Epqlam8kuhDKNmVS3cakEoBANCf1PkkjS7K6MzzSYm4ezL6lsE0V67ylSlXSLxN2O32mINyjEZjUo/pqmPqa0yBJkdXpg9R6aaFzK2//qgXn3MAAMJFXOetJ49zjBjkpGEQkWZ6V/liq9WaQoUobCLeBeWIJkg4K0JdSqrGFKRWmZlYifL6s2QufVIpACCzwp/HqP7ZjOH36tWTJzRKNyTIhdEwlRYXF6u8aizdrSvPi6S+bhU2Ea8fLuK+3nrVXae+xhREVKaOK1EgjcFnZigAANIl3Xwp9SSF/1sNm80m3a1Yf64jS+WRnhFgNBrDU2Bqz5p3uVzxnhQVTRrlI1eIyrpNuAkhzhNiwxUXF2f00fDqJVVjCiIqU5ZUyeOtRCt2u91gMNSr3wMZRSoFAGRWan2lelEurZxCJBEjn5K9jCt3Rqq5OF5aWiq9OPxibsK6VbMJo9EY7x5Zp9MpPT825oyk8aRWG2okVWMKYlammGTJ461EkwPe5XLJw+mypKNUJJUCACBT0/vodDrl8ebxAqKyiEmvEmYg+flJ0mV0NVtJahPh02qaDoiYJD/ZcV3a/vxItsaUV6VcmWpKrrCSdC4OSCJmMasnt6/UDVIpAAAHqezostlsEVfwVZJGrsTMtQqvl+YJCn82lYabEBUnt5ceK6p29zIghd1RXlVSlZnCSlLuK42YY0vNc3cbGVIpAAA4fp3IuAAAIABJREFUeBevHP5sNpu+eRTZhlQKAAAA/ZFKAQDAQQ1rdBoaE1IpAADYL/3BOkDKSKUAAOAg+kqhF1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUobj5qKzdMu7nne2Wf++99nnHHGWed3f2Gjq0bvQiHb1FRsfOGCrmedcca///1/55zX4+KpmzgKAQCqkEobD9/eNXc1yW3SadRiy8pVq6yrv/i10l+rd6GQbWoDlb9+sdq66pOlc/oJhx8h3LVmr0/vMgEAGgRSaePh2/fdva3aH9nlP169SwKIYmDTg4VtivLv/W4fqRQAoAaptPGQUmm7zi//XUkXKfTmK1s7sqA1qRQAoBqptPEglaIeIZUCAJJEKs2QoO/b4lve3FmXF9PrTSrVYd8zoHHshVaSrw1SKQAgSaTS1ASD344b+1l5uV8URdFfWTrzst49uoXpeu45/2gitDvjnC4X9zWudLvKPV+NG/dNIJDJMqWUSmu/HNnvom7du/e7ZuFOj7riqd/33v1uf2fdx2Me/S4YTG2XNC55uPrYgmlIszbEAxXiqhLFULUWtUEqBQAkKatTqb+ydPbll118Ye/+/fv3v7z3hT17HKpnr8suv3LQgLm/VvrD31a788Wrr+w7es7SLT5frSiKYrBm3w/vvfuft8K88eKcO47PP+aWWfP/896KHX6fL7D1g7lj+htGWTM3S06yqbQ2+MWDA6/oO2rha68vXDDylLPu+XhPwjcmt+8li5Zt2r1pybyHr7hK0x1PpeSp70XdtWBq0q0N8ZAKqRFF0a9FbZBKAQBJyupUGqzZ993br819oocgCIJw3mOzzK+FeXX+rBtyBUHo+sb2Krmrr9a365Vr+5w/9KWF33sSrH3fd599ty98gXfday+P6n/Nta/s9GbkCntSqbTWt+vlId1Pvu2lD3aLoli7evAxeSfP/GOfUp9myvvu0XTHUyh5xNvrbQumIM3aENVXSLK1QSoFACQpq1PpftseKWzZ8ojLLK6ov2xd8eR5g5f/UXHgpBv07Xzl6lMumbjYkeq2diya0Ovkq17Z4c3AleAkUmnQ65jf/wyh10ubRVEUxYD31+X//fiz7T5/KP5b0tt3rXY8hZIf8vZ63YJJS7M2xEw2K6kUAJAkUqm78qP+rdu36LvkL09klvP5xBUzvq127z+pBjw7Xh545rHDv92bzvbK1hhPOOca8/bUbv5TkkQqrV19z5n5Jxi/3htUGV802Hdtdjzpkoer7y2YtLRqQ8xss5JKAQBJyvpU6q3+YshRbVsdN/XPyv1XPQM14sZF3wXcNWKwVvxjb0Cslc74Ps/qUZ0LL5+3sSatoTtBz+bxJxT+3z2fe7Q+V6tOpXvfHNK9TcH/Pfe3+lVrsO9a7HjyJQ9X71swSenVhpjhZiWVAgCSlO2p1Fv91ZB2LZsfO2ZD5f5hG7XBfR+NmLuxrPrQvp+AZ9urPY9uc/K0v6rjncF/mGgccfdT3wfl8R9li+4aNuz2YSU7D1mX373xkVOLjurxyla3tp1tCVOpv2rLghuuvaJ3j46CIAjNzjJcfcOCLYeO5Iop0b5nfsdTLXkye1EnO6IJLWpDzHizkkoBAEnK8lTqq/76xiNbFf3r6d3yojXP3HnOwM9cFYcGu0DFbzNPatYq/9oVZVFDQkLeqs/H3XhVnxMFQRAKL3p7l18URfGHR6+9qFAQBEE4t+TvqvALrJ6q1dcVtG76zxmlFZqGmoSpNOh1fj575qSnr+9U2LxZz4cnPT9z1me7vQm7yeLue93teIolV7UXdbojmtCgNsTMNyupFACQpOxOpT73mts65B4hND33misHDBgwYMDAK686r6jJpe9sjwilorvcclmz1q2OfWGnKyqHhGq8G96YPmXypClD2gj5Tc8073T/tch4091PTpj2ktn8yuvvb/LVhJ/FgxW7TMcVtW7a54Oy6qjbAUPeqtXjh94wQMkNQ8evrvJEvVXtFfyK2Sd0aHfsFNW3Esbdd013XI1kS65qL/TYEU2kUxti5puVVAoASFJWp1Kf23Zrx5Z5ba+bPGHi0xMnTpz41LixfYta/Gv6TldEx5PfVfp4h9ZFhTesLFeaPefXmb3yCoR/Xj7w9qenvL0z7ss8rs+GNG9ddGTx5vKoy66hGu9Pr06ZNFHJpBdeWe+pSS2VBqu3zz+7dfsT7l9XoXLeTTX7rsGOJ5R8ycPVXQvWjfRqQ6yDZiWVAgCSlM2p1Odec2v7FkX/uH9T2MItbw2+66uysohU6i37nzG3dVH+/euUzrC1NXve69ms8IjWg80bFDdcZr+/oHVRzt02Tc/XalJpoHLrvNPz2uZctarCrXK1Kva9LnY8hZKHaxAtqJ5CbYS81V89edcttxlHPDi6ZEd1vNSc8WYllQIAkpTFqdTn+e72dgUtmlxlrZRP7H53aO1rK8rd3oiOSJVn2FrPX0uvOLLjqff/4lfqwMrQ+VpNKnVXLr8878jCC17fUXVo8K78+LEnHzfZqnzRXbAqSlsHOx635KIoiuL3T99zx8033zz0tvsem7sp+s8NpAXVU6iNUI3nx5cee6hvkSCcNmtz9O0K+2W8WXWvIwBAQ5O9qdTnWXvnkXmFh5//hqNSPrEHgqFShy8UNfujup42MeBzvHSmkNuszR22ishgG77pMvsD+vSV+iq/vfPotq06Tt4lD0wJeau/eebum/t0EATh5Bf/jryhVt2+Z37HY5R8/6arPD88P+ml58eMHzt2zL3XHy807Tva9FMwMkU1iBZULW5tyCoWdO7Y981tFXHHQGW8WUmlAIAkZW0qrfH8cNeReYWHd3nrd3fip0cGXFufPqaodeGAZeXuuGfngCj+tGzBw/1b5bU8apJDYXS2x7V8YPPWrTpN3BLjvlKf2zZ51D3DlNwzavK31dEpIXEqDVbufLZDUdFRd/+vQu4TDfk8a2c9dv+9/Y9p1a77q3uiU2nifddkx5XFKrkoiqJY6692WBeaf9n/X9/Kpwa0EY68bfXeiNFgddeCdSBubewXqNj2Wu92fd/eqTAuP+PNSioFACQpW1NpwP/T3e3zCg8/983f3Wqm1HGXLzc0bd2qwzPbyuOcnUOi+MvyDzc6veuMx7dr1eXlnV5RFEXvxp9D/ojgEnDtmNSxqHWTKz4ui84DIZ/7u+mPPHifkgfHTfvOHeNSe8KZodw7X+la1P4fxrXVUTmh9r0Lj+nU1ex0Rb81wb5rtOOK4pe8NiSWVR+y4Mubj25/2rS/9kXNolBHLZh5Su0oiqIYKP/11d5t+7y64ef3Hi0eO8JoHDH6gfccERPwZrxZSaUAgCRlayqtWD6gfU5BYZ+PVZ4wg5XbXz6raVGhYUXlIaNLAtW7lk+fU/JrSCz75MV3lq4X/W7fj8M7tW3R7bXfffs+nfj2iq/dkWdxj2vZ1YVFTf5vwdbKtJ4xFClRKg1W7Vh4Tl7REZctjx4hU/H3wm6dOnZdECuVxtz39Ha8KiiKQe9fK5579qmnnn3+C6VR8QlKHslT+dUtbf95z+e7I+NiZlowzo6EarzrX570zDPPLrDvnyxBxZKAygpJVBuhin3Wm9te/t6W3Tut95918nW33z7igTGLd0am0gw06yHHM6kUAJCkbEulAfeuD8aPu++uQR0FQRCEM4be8+D4Gd/H6HiMFPTtWmo4uvUJI9ZWe8MWe5xfDhEE4Zyhd8xbOHWD6A+Jot+99q4jBaGjYei0Fx/6798eb+Sa3H9O+Wfb4658f2fSE58rS5RK3ZUrrypo26nf4u1R3cOKqTTmvqe347WiKHr3fHuTIAiCIPzzriU7PUp1oVDyQ/m9uz+5/ryhs793R0/nmpEWjLMjoYo9s48UBEFoN/NvqUYTLAklUSEJaiNUXW5/9Mwr39lR5Vo9bcXSlZGlV6wQDY9nUikAIElZl0qrHe/cN+LOYXfd9/DDDz/88H3DhxlHPhvrJs0Yb/V//2i3/GNGrgufbCfg+eOjx8c/MOKe+xbJTyOvqd30YvG4UfeOfC/WA8oDNfbnLsq/4PH/+bS+IzFBKq2pXnffKe2OGfadO3o8tXIqjbXv6e94wPPHR089MW7c/Q+OvODedWXV8e/uVSp5uNpA7U+Trh44+8dgIGZ7ZqgFY+1IjeeHOcWPPlo8+4f9v3hCiZeorJAEtRGqLt/w+HkDLH/98OoC87j39kT2bCpXiIbHM6kUAJCkbEul6Qj6dy++q3uHgZ8401nLnpWDO5w37N0/arTtKBUTpdKAZ/3oY/O7PPy1O0bnWaJUqsW+x9/xL4bP+WNfVfw5VpVKfoj1L40ePm19yB9vVZluwQQ7oprSehLVhq/K/tAp+Z1vGHZJx389tT3BT59MNiupFACQJFJpMkLBvUsf7Ntv5PyYU2Kq8at5VOczblvsDKQfXaJEpdLamrI1Ly948wdPQBTFvdYbj+52e0nsNJwwlaa973F3vGz5lOenrXUfOu2m+pKHreijZ14Y+6xdrFGq2gy2YMwdSUH0epKojZDXZR930tF9bho4oP85rf5p2r4vQY98ppqVVAoASBqpNGk/Tb772juemriyItk3Vq6cOOGK8wcZ398T+wJzuiJTaaB809wuucfd+b9aUfz1pdHnnnbLot1x0rCKVCqKYqr7HnPHQzXuH80vPP3YgjkT14u+iDlI1Zd8v+D385e9O+5z+f/O//0W9MWby0jbFlTakWTEXU8ytVGzb+es//v3A3YxFNo7/V9H3/rp3ioVm9awWQ8ilQIAkkQqTcX3E28bOnaieb1b1axSoigGPZ6fXn5mzOBbhr+7J2Olikyl/oqN5t6djr7uqfFPTCg2Dhu59G+FNBx898Jjju5RkjhUJbfv8Xc85K20jrt92C0Lf49+V1IlF0W/beaEPk37PP3SSy+bX3rxxRdnzZo39qlvqtwK3YQatqDSjiQj7nqSqI1gpWPJoA43rHB5gn7Xl8NPO/fxnypDO5bY9iUcz6dVsx5EKgUAJIlUmqr1zz37bYXC1OKHCFRWfvvsc/bMlsi377sRrTocef47Ybnip+eNQwYMuOGmt+KH4aB3t3XGpMevbCkILa54/Nnp1t0J5wZQv++p7nggtD5xyUVRDHp3r3j2vtOESANXVyaabqoetmDcjausjUD5pgU9mp03e8O+WlH0Vnx9Z6cOV46ZYn6q5PcKNfOqat2sWx4qbFOURyoFAKhEKm08vHvXDBMOE5pf9eQzU6ZOnTZrzudONXHAX7X15SE3XDPopqFDbxp0zfWDF2yp1ON5RanxV21deOOQIUNvuTXMXSMmra1JNDKqMapx/frGyAeXOiprRFEUfTX/m3DXbTc+uLpuJ/oPep2r586eNvW5ifedJQiCcIdtbza2BQAgeaTSxsNfveM/dw+7+YZr+ve//PLLrxx43Uu/NaB8iUbCX7XFfP2gKy+/vP+Vg24cOuzut7ZXcRQCANQglQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH//396dBzZRJnwcH+/F1/XV9eVYj1VWUQKL7iqstyIscmgQ0CoqAlpZqxxaFVY0eHAJGkQQkC0FiiAV5bBBaouEUxvkkAhSbLEtAaSUo53eV5rn/WPoMORq2iadlHw/f2E6mTwzMU9+eU5SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAABEjIL0fT9aU1NSUlLXrduw5fvN1rVrU1JSUlLWbbJuy/nthEvv8iGikUoBAIgU8vyho7q0+Wu7du3aXXNtm0v/cHHra9ve0K5du3ZX/7H1v94Ys96pdwER0UilAABEiOofYqctHz9vV05Ozs6EpaNubn33zO9sWTk5OTmJT05PnDYvV+8CIrKRSgEAiAROIX6wbvt5V06lEMK1K2negL93nXvsmPLHIz8fOvrbb7qWDyCVAgAQCVxCHDkhKouFEELeumx8v47dPzxQXqz8sVLUOEWNnsUDSKUAAESa8vUrx/Vof/0r35dWML9JCCHLst1u17sUIJUCwNns1wx7ymd7Q/salUeyt86dNOW9saNffzU2NjY29q2F1h3HQvua8MXlcu1dNmuS8k689sZbb0+aF78qu6r6jIPKt84Y3+2ma/onHyyN0OZRq9VqMpkkSZIkKSYmxmq16l0iCEEqBYCzQ/5+e/InHkwP9+1huNQ4+aMZ8+Yu23uiMCQzrAu/X2VqKUnn3XjnvT169uzZs+ejE5ZZD4filVA3l8u1dfrrz/bu2bNnz573d7j2cqnVNb1WF5aecVD27DcGG25+cGVxGXPuEU5IpUBEs9lsehehGWgWXXsHvls6+lYPHVq3vvgc6YLr/v6Prvc88/X+w5WheOmitBWm61tedd/SLBpIw0xh0vRhra409P+24IxUWvCNKbp/p34J+aLa1zMjRjOqA202m8Vi0bsUoUUqBSJXRkaG2WzWuxThTpZlo9Godynq5qpxVld6OLAowTTIOGpLUWllZVWNKzRDCIvSVo67odXV9y3OOMIYxfByYuW051tf3WHAmam0dNl7wx67udfnLAPVLOrAjIyMuLg4ZbCBw+HQuzihRSoFIpfRaAz/Gll3yuAzvUvRYNXVZUVySWhf41QqvXdR+qGq0L4S6ilv+YfRHqm0as2wYU/27rGgSL9yhYtwrgPz8vIsFovRaJRqhW1Rg6j5VrUAGstsNjfras5sNufl5Xn+O7iaeSptCqTSsOUtlf46b3jnzj0eNx/RsVzhIjzrQJvNps7EkiTJaDQmJiZmZGToXa6mQFULRC6LxRKGNXKAlC4tk8nk9u+gs1gspFL/SKVhS02lhVVCCNeuicOG3tn+8haSdP6l19/59DPjtpeFZKRxsxGGdWBMTIy6MoDFYgnRj+2wRVULRC5lbRS9S9FwTdNWarPZmjKVNu5CSp1p41595cVX1+Zlq/mwND19TnR0TOywpY5DoenKDyiV5i6OGxcdPcu6rvD0Y2Xfvv7uS9HRUzd8F5TBcieXL50QHT1t9Tf5px+rXj9uyojo6AnJSfuD8RKhlr9y6YToaLNl9cnTjzk3vD11ZAMvQU2lcoUQwnVozeKEqeYZs+b+99PZH0+dvzDpQFVkT3cKtzowIyPDZDLZbLZIC6MqUikQuTIyMmgFrJPD4Wiyu9SIRt/CzF1fvvPCY093kiRJkv45J2mPLIRwpv+4JvrOO8+VJEmSbl267UBIMojfVFpVWpg2553RI3v9sYUkSa0Gvrw4RwghSkt+mRr93LWSJEnSecPHrSz0eGLgnJXlO+ZPNo00trxCkqT/fejZT/cLIYSzJnPmy8NvVBqeBr2UkF/XeXTkrKrYueB908i+ra6QJOnS3oNnZwohhLMmc1bsyEZcwuke/BCPLW6mwq0OVAaSNqNlAYIujN4MAG5C3RbYlHmrubBarUajUZsLfd0lq9VqNptlWQ5uAfy80W7/P5z5x5O7v//0OeMjxj79br9YukDq9B9LdvnvP2ycNOzR7gMGPDlkyOCXXjCtP5TntS2zSuQkfbVgWt0WfJWUIzxP4TeVVhblp77z3MCH+w/sfl2bP0ktu8YsPVySd3j5Oy8+1Nc44JlBg4cMiVmUamtMZKyuKN34/kuDH+73xIPtrmspXd7libn7y+Tjye+/8sgjD/cb9PTgIUOGxa2yhvOyVdWVZZumjDh1Ca2kyzpHzcksk48nvx/buEvwOtsJqjCpA9WZ9dru+7N+ur1X+r8ZALxqgnGTYVIjhw91hoHbUlBe75JysNFobJq+Ns//H7wfV1GweVj7K1tI7V+d8oH5RdNHkz/OqevUrnKRNmZEVJe6RY0YkybKPFZ/CnBc6e/xL/Rqff4fez31ypwPp73zzGibCPok8ONfjH3sygtb3Nf3xdkzZowbGLuptNn1gx7/YuzjV13Y4p6HT13CxpKjjTgbqdS/YNWBsiw35jeq+iPT4XCYzWZ1nlNiYmLjy9a88IUEhK9QtJVqF4SXZfnsSKVBqbu1k17dth/0epckjWAts6+01Kqzbt2+5Py2larKyzMm3nX9pZJ0RduYRUsOBraAqMvpdFbXzel0ejtfoLOdKixvDe54jtTikjufH5NWE5JpNtXWD0bceq50UYtbnhi+3lkcipcItWqreVTnc6WLWnSKGr7e2cjgTir1X3MGqw5Ufig2+DeqWxkcDodaHYXVsNcmcDZ8IQEIkNLprH0k/FOpw+FQV5COi4vzPMBqtUqS1MhlU5Q74+sbxVcPvqidCxWUYKqNxWovXgPOU1NmN//z+oulfm8tPlDRyDIFJuA5+EXr/tP/BqndPS+sKwjVcvtlP0we3Omca//xpOV4c93gvSxtytCbz/3LLQOTGn8JEZ5KA+llCkodqLZx1jeY2u125Ymef1JW//D8kXx2C/cvJABBFBMT4xZ0GlAjx8TENM0OnEqnmFtQ8wymSphrTHOpLMv+n+55l7R3QA3NjUnGdrtd8qYh53IWZbzZsdNFrQbOSm1M52/gAl8ZqmTF8Gf//AdD/0k/hawwFd+++XLbC//a/Y3vhfAVfWuUlmFnTXhuRVWRYopte2Hbbv/xcwkBivBUKgLoZQriL3MlRwbYuqnkUWXtJ19lUKqFZrG3XLCQSoGwIMtyvcYk5eXlea37lA4p7am0/6k0K2qPr2+NrFSgvpoGrVZrsLZpzsjI0G5q4j+oeabteqmzzJ4vajKZ1Ayq3HOj0Wg0GrV33lfS9XqXlPytNoooU4M9LyqQO1xTmbOke9urJOmymP8ubZJYGnAqrdo+8dn7z5MuuG1A7M9edmCvSt80/8kBT72+8WhBgxsJa/bMfLn3BdL57bs//6Mo9ZbpSnK3f9y96z1dunS5454RX20Kv6XJa36ZFdvnQun8G7s9/6MoaVwsJZXWKfA6MJCfvkqOrPNDGhcXp61C/ZShiVem010EXSoQzurb1JeYmOi1Z0dpt1PrO6WKVNsXPX+UB17f2e12tVHQa0eVsl+8JEmeLRNGo9F/J5QSwtRIpxTbbDbb7Xb1bEqLptf0qRwfyFV4bc40mUxms9lPS6fksf202rMmy7La+6+91cp3iefb6usuORwOZfqtMpxUeX/dnu7nDp9W7hI/pVgTRt3V8f+kLu++uc3ngae5KsSOyaaYh+sWY5q8Q5Q3dLaT2PeLI/mD56I6SS3vv2tRWeUZybPm168Xvvb39ldIF11yx1LHMWcA5fYmMzM3ZfqIIZ2ly27rOLtA9ihOwe5DW16dumTqtBnTpk2Lvu62e27pNz/plwa+WGhkZuamTh85tIt02a0dZxcUNG5fAlKpG8+PZIBVh/KJDuRXd537blgsFpvNpq1S/Pe0RNTQUlIpEBbqzG0qJQap6VBd2U6NMtrIqK4zotTFeXl5DejBt9vt2mZLdSSlW9uedgK7GovdHrdarQ6HQ9ugKMuyzWZTzm8ymdSQp62yHQ6Hw+Gw2WyJiYme3wp5eXl2uz2QiluWZa9Vv7YhVtsIqj3AZDJpi+Q2tEC5Xm04Vg+Ii4tTQ6Svu6Ry22ZQe6PqfK4QQohSp2tPytd7c2uyE4Z2ueKiB5//JEcIIZyiPP0XV4WPUaauSpEeP3viyLpNnB2fLiobmEr3//Ddzl+P52xf9srN0jUdnrIq68RXZGZWHz8uRM2B9UmzXnzk8a63XNd7hSOvYeuqZm3bvGPPoazdKe/eKbX5y8NJB8uFEKIyK6vqaK4QQpTm5tjWJS7YUHCq3Otm/+vK1ncMGbe7oSE4+LK2bd6x+1DWnrXj75ba/OXhpAOlQghRmZV96hLqKQJTqZ/xRUrN6XZAgKlU/URrP5XaT7dCqY78d92o1bKW0Wh0q3kcDofSy8+4UgAhpNSMnrO81dZBz2XqlOrJYrGouUQZkORZtdXJramgzhpZacXUEj5qVak2dzagVI0XyJhOX2tTe16O2wGe85B80YZjbdOyVoB3yesABu1z1RdyOTZtTFq9aV+5KD/w3dJli7e6TpSK/Dn9u/9Pi7tGfOXIP3T42+lJ320tLS+v8xY1jM9UKmdlWhNXbMh1ifL0tSvmWbLsJ4XYOmf8dVKbv41ILnQWb1+YvGrVb0dqxxm4Nm8zd2vXZ/lvv595noLsrHXx8fELFiT8VFB2ZmAtPuTYlPjV2oNVoipz49fxK/ZuyxPCvnRGR6nltYO+PFpd8tPi1JXL0h2HlIPz5V/2CKG20haSU9n4AAALe0lEQVTMfLTr3QP6LHTP69XZG7Ysj4+PX55i1X4Yc7dbV8bHx69O232itsQ1NTkbvk6Mj1+4bk9W7U4A1WUl6Ws+XxQf/3ma42jZqVtx6hLmL9yZ7+MSKkXV/k1J8Sv2bs0TYvcXn3SSWl47aFluVclPS1JX1F5CPUVaKnU4HP4bHSWPsemen3ev1J4KbRXq68Prvzpy+2j7Gqqk/jWQ4p01SKVAU1OrG+2DgeceqbYpVK0ltZRUpP6sV2o0bcOeZ0+0/9LGxMQYjUaLxZKRkaEMoMzLy9M2l3rWxW7Xory60gjqq/41m81qQ28DeJ2b78nXakpKM7NaNs+vAV/X6+sOaF/R1zH1ese9Pld9FedXj0Vdc3HbHu8vnP3NnDG28oJqIYTInhJz+3lS50de+mj8srnPLjoSwmWSfKbS9CXzu0vSNc+9/9GKmbHr920+KoQQheu+HtNauuzP3d6dvsTcf96un/aqIfHkGuv4+2/oszzryJnn2Z2w4F7lBtwz+st9x7TpOvublX2lc1o9+a75q1mvW3etOSSEECVpGye2veDiS+58c9qiDwfM27ppl4/G0JK5PZ8Z8nj0Ovem2dKFDw64XJIkqeVNY5YfqKhdD2vti91aSZLU7qlxtb9vnNUVS3pdfa4kSfe/t2Bf7bOP5HzQUZIkSXr6i3W/K4/9sqj2Eu56fVm65yWc23LgO+YvZ41WL8G2efL1F1x8yR1vTEv4YMC8rRt3OYUQpbk521avXL1+c3axSwjh2L41df2vBzW7k7qLtFRa51Ao5WOufUQKeBRTXl6e0WjUtoN6/ZAGMhxL6RFSP9R+ar+mmVoaPkilQFPz2laq1Hd1hhKTyaStpJQ5T8qfYmJi1HMqPfVqP68sy8phXruM/ZdW8pg7lZiYqIyhVPrN1YJp865aySoVtHZ8gvIUbUOvWokre1JrrzcmJkadFpCRkeEZ8sxmc+C789X59eNwOBITE32tP6VtUjWZTMp90C5c5dnRpuR49Ymed0l902NiYpSuOiV3KqN4tXfD87nqv52bJ384wGAw3G98fHFOldq/nr5w0tCOHTrcO+zVz082diq3fz5T6cFN1lcNHQwdDB3GJe9S9yU6+dPGKd06drj59nvf3nHkuPb4E9+s85pKs1KSRxo6Ggw3tb+l9TPfpv6qGZGau9P2luFWg8FgGL18y+HaR+V9O2b26dihwy23jd6c9bvPglekDH/ijVfHrfYY2lD+7WvjHjIYDIa2tw287/2DxaeKs/2jVx4xGAxPTZi/99SBTmfV2tGPdTUYbhmVkFz79pSfyE0ccndng+HeCdadp1pVs1OSRxr+ZjDc1P7m1oOSPS+hs8FgMIz+crPaICpn7PykT8cOHTrd+vrm/bUXlrs5Kbb9dQZDx4/25+/b8/Nbnducf9voT7/3eYERl0rrrNA8Jw/Va9Sm29OVDUK1lVWDNwtVBs1rP+8WiyUCt3cilQLhQpZli8XiFryUsGKxWOx2e9A3twyEZw0uSVJ9N49WLkqZUaTj0v1ec7kbu90exE2om+hiq8sriuWC/PwCWdsE56qsKJML8guKSkK9aqnPVFpTXV0mywUnT5woqdLkYmdNZbFckJ+fX+I21d5XKq2pqiotLCo8fiwv7bWJe3/YqUlYNU5nmVxYcPLEieJKzUJPNTVVxbKcf/JksZ8hoyXJo1+ZPW3mHs9DXFVlZcWFxUVZ67dsGT35YEmpUlBneWlxYWFhSXmF5ilVZSVFhYWFpZVVtVfjctVUlhQVFhYWlVfXNrPWXsLxY2mvT9rT0EuoqSyzf73y47595ny//r1eH49+6Mr7Zs772k9vcaSl0jo/brIsN2a9DiFEpHWpNzFSKQB/3Gp5tY2zXlWzWw+4XlNK3XrfPNU5eba+lNbfIJ4wPAW+XmkdfKXSU0qrDs0d+mXO2uxgrI/vrMiNH7b4m0SbvwX9t234MWFIUlVR0PahKqs+PPfZL7MbfgnHU3d91qNT9Hv9x3+/Yd/Rw/t/Ly0q8TM1LAJTaZ2/PBuztrGyWn6Dn446kUoB+OOW0tTBrPWtmsNhc2elq13ZBUDb8OxwONSeuGCttypq1+EK4gnDVm0q/Wzf4YbNna/lN5UWH/s1btj81K15JxqfEZ0lIu21hasTNuT7OVn1jlUps2JX7CkU1UEaAFFy/Ne4f89PsR1t+CX8Mifh6Suu7rcoeVdAPwCOrTA/H0mp1P8vT2X9tcaM1DSZTEHsS4EnUikAfzzbDpVBqw1YrMRmsykDAPQaLOVr/yRVgLOmAqEOFI6EkWFFaStMN7S+5oHlvzdyRlX+mvWTunmm0uJd6+Y9+lD/vtMTluS4ChvXHCuEEGVCXjXj29XfyMIphBBH82syT496FUIIkbNo/Kiejw7+d9KOjSe8nqK+in+2znv0of59py1cklMjNziTujIXLZjyj8Hx1kB3R6hMmf1Sm6siJ5Uqv37doqeyWpO6VX3DzqyMzqf7PtRIpQD8CbBHW7utX51b/OnI11zXwNeLDYS6MZVbi3JzuUv1VbhlxZt/kqQ/3N7v8Wejo6Ojo0fNSbY1ZFepopTUV/52Y99Ut9hWlrnd8uaYCZPWBmWnKueRRW8Mv6P1gDcGv/z222PHjh09yjx/lb3gjGOOJC+YHvtugiUzGC8ohBBl+3dY3hwzfmJqQxYdPW3vwjnx/Z5O89v973K5fk6YOnZYdHR0dPST93S6UvrTlT0scmSk0jp/eTasoVRdPySilg7VBakUgD+BpFKlEUIZQKn9d3hSFqZWv6XqNYu/Tl73MlA0r7tUL+U56WvfeWH484MGPhEVFRUVFTXUvGrTkfqdo+bw1nWfvdDnwT9LUst+sWP/u+XIieBP0irNzLaOfWFAmzODyi3/em1n4xtgm0D17vEfzhj6UKr/G+NyuXZ8+vbIx6OioqKiHn968LMvTJgwL72iWVxhMPj55RnIqsZabuvZnTUf2HBGKgXgz1nWVhpSyhqEyvIuXr8CuUu+1ez94tNR9z3wQI/evR7s+kCP6EUZB4O/wKqctmNuv/u69ujdu89pI+I+2RH0VwqJis0zN345bk1JaNf5OgtoF60zGo1um84HSPk4azcuCUVR4YZUCsCfAFNps0NABIBwc3Z+3wAIlkau7ReezuLOdABovkilAPw5W6eQ01YKAOGGVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/kilAAAA0B+pFAAAAPojlQIAAEB/pFIAAADoj1QKAAAA/ZFKAQAAoD9SKQAAAPRHKgUAAID+SKUAAADQH6kUAAAA+iOVAgAAQH+kUgAAAOiPVAoAAAD9kUoBAACgP1IpAAAA9EcqBQAAgP5IpQAAANAfqRQAAAD6I5UCAABAf6RSAAAA6I9UCgAAAP2RSgEAAKA/UikAAAD0RyoFAACA/v4fu/W1Vo23NRkAAAAASUVORK5CYII=)
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تمام روشها نیاز به دانستن اطلاعاتی بیش از صورت مساله هستند همانند وزن مناسب، پاسخ هدف، ...
تقریبا تمام روشهای کلاسیک پیشنهاد تبدیل روش بهینه سازی تک هدفه به چند هدفه را دارند!
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پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتیپاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی
aryan.filenab.com/product-64998-behine-sazi-chand-hadafe.aspxCachedپاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی. چند تعریف. بهینه
سازی روندی است برای یافتن و مقایسه کردن راه حلهای ممکن تا وقتی که پاسخ بهتری ...
پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی
marketshop2.lineblog.ir/.../+پاورپوینت+بهینه+سازی+چند+هدفه+بر+اساس+الگوریتمهای+جمعیتی+Cached10 ا کتبر 2016 ... پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی در 38 اسلاید قابل
ویرایش با فرمت pptx. چند تعریف. بهینه سازی روندی است برای ...
دانلود فایل ( پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای ...
www.newfilestant.ir/paper11443.htmlCached29 ا کتبر 2016 ... با سلام،محصول دانلودی +{{پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای
جمعیتی}}+آماده ارائه به جویندگان عزیز میباشد.با کلیک روی دکمه ادامه ...
برترین پکیج پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای ...
gooyadlc.blogroom.ir/post/418Cached19 نوامبر 2016 ... محقق گرامی،شما با جستجوی پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای
جمعیتی وارد این صفحه شده اید محصول دانلودی(پاورپوینت بهینه ...
پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی
downloadf.aramblog.ir/.../پاورپوینت+بهینه+سازی+چند+هدفه+بر+اساس+الگوریتمهای+جمعیتیCached14 ا کتبر 2016 ... دانلود پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی. به سایت علمی
ما خوش آمدید. فایل دانلودی با عنوان پاورپوینت بهینه سازی چند ...
برترین پکیج پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای ...
agigdlc.ir/post.php?id=11243Cached18 ا کتبر 2016 ... به صفحه دانلود فایل(پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی)
خوش آمدید برای دانلود به ادامه مطلب بروید.شما پس از پرداخت هزینه ...
پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی
ashoradl.blogroz.ir/.../پاورپوینت+بهینه+سازی+چند+هدفه+بر+اساس+الگوریتمهای+جمعیتیCached11 ا کتبر 2016 ... پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی -
پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی
selludownloader.avablog.ir/.../پاورپوینت+بهینه+سازی+چند+هدفه+بر+اساس+الگوریتمهای+جمعیتیCached6 نوامبر 2016 ... در این ساعت به روز هستیم با دانلود فایل پاورپوینت بهینه سازی چند هدفه بر اساس
الگوریتمهای جمعیتی که محتویات آن یاری گر کاملی برای موضوع مد ...
دانلود پاورپوینت بهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی ...
www.farazwb.download/?p=15579Cached24 سپتامبر 2016 ... بیننده گرامی سلام.به وب ما خوش آمدید محقق گرامی،شما با جستجوی پاورپوینت بهینه
سازی چند هدفه بر اساس الگوریتمهای جمعیتی وارد این صفحه شده ...
[PPT] Powerpoint
saba.kntu.ac.ir/eecd/..._/presentation%20of%20multi%20objective.pptCached
Similarبهینه سازی چند هدفه بر اساس الگوریتمهای جمعیتی. مهدی علیاری شوره دلی. سمینار دوره
ای گروه کنترل. آزمایشگاه سیستمهای هوشمند ISLAB. آزمایشگاه سیستمهای هوشمند ...