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generation، Fifth، app=”EN”، ADDIN، name=”Conference، EN.CITE

list</keyword><keyword>reasoning loop</keyword><keyword>schedule</keyword><keyword>short term unit commitment</keyword></keywords><dates><year>1996</year><pub-dates><date>18-21 Aug 1996</date></pub-dates></dates><urls></urls></record></Cite></EndNote>[18]، ADDIN EN.CITE <EndNote><Cite><Author>Dasgupta</Author><Year>1993</Year><RecNum>27</RecNum><record><rec-number>27</rec-number><foreign-keys><key app=”EN” db-id=”fa0avwsf5ptrz5eatervprd6r00v0zsptp2p”>27</key></foreign-keys><ref-type name=”Conference Proceedings”>10</ref-type><contributors><authors><author>Dasgupta, D.</author><author>McGregor, D. R.</author></authors></contributors><titles><title>Short term unit-commitment using genetic algorithms</title><secondary-title>Tools with Artificial Intelligence, 1993. TAI &apos;93. Proceedings., Fifth International Conference on</secondary-title><alt-title>Tools with Artificial Intelligence, 1993. TAI &apos;93. Proceedings., Fifth International Conference on</alt-title></titles><pages>240-247</pages><keywords><keyword>economics</keyword><keyword>electric power generation</keyword><keyword>genetic algorithms</keyword><keyword>load distribution</keyword><keyword>thermal power stations</keyword><keyword>commitment</keyword><keyword>generation cost</keyword><keyword>load</keyword><keyword>on/off states</keyword><keyword>power generation</keyword><keyword>thermal units</keyword></keywords><dates><year>1993</year><pub-dates><date>8-11 Nov 1993</date></pub-dates></dates><isbn>1063-6730</isbn><urls></urls></record></Cite></EndNote>[19]، ADDIN EN.CITE <EndNote><Cite><Author>Libao</Author><Year>2004</Year><RecNum>28</RecNum><record><rec-number>28</rec-number><foreign-keys><key app=”EN” db-id=”fa0avwsf5ptrz5eatervprd6r00v0zsptp2p”>28</key></foreign-keys><ref-type name=”Conference Proceedings”>10</ref-type><contributors><authors><author>Libao, Shi</author><author>Jin, Hao</author><author>Jiaqi, Zhou</author><author>Guoyu, Xu</author></authors></contributors><titles><title>Short-term generation scheduling with reliability constraint using ant colony optimization algorithm</title><secondary-title>Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on</secondary-title><alt-title>Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on</alt-title></titles><pages>5102-5106 Vol.6</pages><volume>6</volume><keywords><keyword>cooperative sys–s</keyword><keyword>optimisation</keyword><keyword>power engineering computing</keyword><keyword>power generation economics</keyword><keyword>power generation scheduling</keyword><keyword>power sys– security</keyword><keyword>ant colony optimization algorithm</keyword><keyword>economical efficiency</keyword><keyword>optimal unit commitment</keyword><keyword>reliability constraint</keyword><keyword>short-term generation scheduling</keyword><keyword>sys– security</keyword></keywords><dates><year>2004</year><pub-dates><date>15-19 June 2004</date></pub-dates></dates><urls></urls></record></Cite></EndNote>[20]، ADDIN EN.CITE <EndNote><Cite><Author>Gwo-Ching</Author><Year>2004</Year><RecNum>29</RecNum><record><rec-number>29</rec-number><foreign-keys><key app=”EN” db-id=”fa0avwsf5ptrz5eatervprd6r00v0zsptp2p”>29</key></foreign-keys><ref-type name=”Conference Proceedings”>10</ref-type><contributors><authors><author>Gwo-Ching, Liao</author><author>Ta-Peng, Tsao</author></authors></contributors><titles><title>Hybrid immune genetic algorithm approach for short-term unit commitment problem</title><secondary-title>Power Engineering Society General



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