ﺑﻬﺒﻮد ﮐﺎراﯾﯽ اﻟﮕﻮرﯾﺘﻢ ﺗﮑﺎﻣﻞ ﺷﻮراﻫﺎی ﺷﻬﺮ ﺑﺎ ﮐﺎﻫﺶ ﺧﻄﯽ اﻧﺪازه ﺟﻤﻌﯿﺖ و ﻓﻀﺎی ﺟﺴﺘﺠﻮ

اﻟﮕﻮرﯾﺘﻢ ﺗﮑﺎﻣﻞ ﺷﻮراﻫﺎی ﺷﻬﺮ cce ، ﯾﮏ ﻧﻮع اﻟﮕﻮرﯾﺘﻢ ﻓﺮااﺑﺘﮑﺎری اﺳﺖ ﮐﻪ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻣﺎﻫﯿﺖ ﺗﺸﮑﯿﻞ ﺷﻮراﻫﺎ از ﮐﻮﭼﮑﺘﺮﯾﻦ ﻣﺤﻠﻪﻫﺎ ﺗﺎ ﺑﺰرﮔﺘﺮﯾﻦ ﻣﻨﺎﻃﻖ ﺷﻬﺮی، از ﻓﺮآﯾﻨﺪ ﺗﺸﮑﯿﻞ ﺷﻮرای ﻋﺎﻟﯽ ﯾﮏ ﺷﻬﺮ اﻟﻬﺎم ﮔﺮﻓﺘﻪ ﺷﺪه اﺳﺖ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﻣﯽﺧﻮاﻫﯿﻢ ﮐﺎراﯾﯽ اﻟﮕﻮرﯾﺘﻢ cce را ﺑﺎ دو ﺗﻐﯿﯿﺮ ﻣﻬﻢ در آن ﺑﻬﺒﻮد ﺑﺪﻫﯿﻢ. اوﻟﯿﻦ ﺗﻐﯿﯿﺮ ﻣﺮﺑﻮط ﺑﻪ ﮐﺎﻫﺶ ﭘﯿﻮﺳﺘﮥ اﻧﺪازة ﺟﻤﯿﻌﺖ ﺑﺎ اﺳﺘﻔﺎده از ﺗﮑﻨﯿﮏ ﮐﺎﻫﺶ ﺧﻄﯽ ﺟﻤﻌﯿﺖ lpsr اﺳﺖ. در اﯾﻦ ﺗﮑﻨﯿﮏ، اﻧﺪازة ﺟﻤﻌﯿﺖ در ﺗﮑﺮارﻫﺎی اوﻟﯿﮥ اﻟﮕﻮرﯾﺘﻢ ﺑﻪ اﻧﺪازة ﮐﺎﻓﯽ ﺑﺰرگ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﻣﯽﺷﻮد ﺗﺎ اﻟﮕﻮرﯾﺘﻢ ﺑﺘﻮاﻧﺪ ﻣﻨﺎﻃﻖ وﺳﯿﻌﯽ از ﻓﻀﺎی ﺟﺴﺘﺠﻮ را ﭘﯿﻤﺎﯾﺶ ﮐﻨﺪ. ﺑﺎ ﭘﯿﺸﺮوی اﻟﮕﻮرﯾﺘﻢ، اﻧﺪازة ﺟﻤﻌﯿﺖ ﺑﻪﺗﺪرﯾﺞ ﮐﺎﻫﺶ داده ﻣﯽﺷﻮد ﺗﺎ ﺳﺮﻋﺖ ﻫﻤﮕﺮاﯾﯽ اﻓﺰاﯾﺶ ﯾﺎﺑﺪ. دوﻣﯿﻦ ﺗﻐﯿﯿﺮ ﺑﻪ داﻣﻨﮥ ﻣﺘﻐﯿﺮﻫﺎ ﻣﺮﺑﻮط ﻣﯽﺷﻮد ﮐﻪ ﺑﻪﻃﻮر ﭘﯿﻮﺳﺘﻪ ﮐﺎﻫﺶ ﻣﯽﯾﺎﺑﺪ ﺗﺎ ﻓﻀﺎی ﺟﺴﺘﺠﻮ ﻣﺤﺪودﺗﺮ ﺷﺪه و در ﻧﺘﯿﺠﻪ، اﻣﮑﺎن ﯾﺎﻓﺘﻦ راهﺣﻞﻫﺎی ﺑﻬﯿﻨﻪ اﻓﺰاﯾﺶ ﭘﯿﺪا ﮐﻨﺪ. ﺑﺮای ارزﯾﺎﺑﯽ و ﻣﻘﺎﯾﺴﮥ ﮐﺎراﯾﯽ اﻟﮕﻮرﯾﺘﻢ ﺗﮑﺎﻣﻞ ﺷﻮراﻫﺎی ﺷﻬﺮ ﺑﻬﺒﻮدﯾﺎﻓﺘﻪ ﺗﺤﺖ ﻋﻨﻮان icce ﮐﻪ در اﯾﻦ ﻣﻘﺎﻟﻪ ﻣﻄﺮح ﺷﺪه اﺳﺖ ﺑﺎ اﻟﮕﻮرﯾﺘﻢﻫﺎی ﺗﮑﺎﻣﻞ ﺷﻮراﻫﺎی ﺷﻬﺮ cce ، ﺑﻬﯿﻨﻪﺳﺎزی ﺷﺎﻣﭙﺎﻧﺰه، ﺑﻬﯿﻨﻪﺳﺎزی ﺑﯿﻮه ﺳﯿﺎه، ﺑﻬﯿﻨﻪﺳﺎز ﺳﯿﺎﺳﯽ، ﺑﻬﯿﻨﻪﺳﺎز ﺟﻔﺖﮔﯿﺮی ﺑﺎرﻧﺎﮐﻞﻫﺎ، ﺑﻬﯿﻨﻪﺳﺎزی ﻣﺎر و ﺑﻬﯿﻨﻪﺳﺎز آﮐﯿﻼ، آنﻫﺎ را روی 29 ﺗﺎﺑﻊ ﺗﺴﺖ از ﻣﺴﺎﺑﻘﺎت ﺳﺎل 2017 ﻣﺮﺑﻮط ﺑﻪ ﮐﻨﮕﺮه ieee در زﻣﯿﻨﻪ ﻣﺤﺎﺳﺒﺎت ﺗﮑﺎﻣﻠﯽ cec 2017 اﺟﺮا ﻣﯽﮐﻨﯿﻢ. ﻧﺘﺎﯾﺞ آزﻣﻮنﻫﺎی ﻣﯿﺎﻧﮕﯿﻦ رﺗﺒﮥ ﻓﺮﯾﺪﻣﻦ و رﺗﺒﻪ ﻋﻼﻣﺖدار وﯾﻠﮑﺎﮐﺴﻮن، ﮐﺎراﯾﯽ ﺑﺎﻻی اﻟﮕﻮرﯾﺘﻢ icce را ﻧﺴﺒﺖ ﺑﻪ اﻟﮕﻮرﯾﺘﻢﻫﺎی ﻣﺬﮐﻮر ﺗﺄﯾﯿﺪ ﻣﯽﮐﻨﻨﺪ.

improving the performance of the city councils evolution algorithm by the linear reduction of the population size and the search space

city councils evolution algorithm cce is a metaheuristic algorithm inspired by the formation process of the supreme council of a city due to the formation of councils from the smallest neighborhoods to the largest regions. in this paper, we want to improve the performance of cce by applying two important changes. the first change is about the continuous reduction of the population size using linear population size reduction lpsr technique. in this technique, the population size in the initial iterations is considered large enough such that it can explore wide areas of search space. as the algorithm progresses, the population size is gradually reduced to increase the convergence speed. the second change is related to the domain of variables, which is constantly reduced to limit the search space, and so the possibility of finding optimal solutions is increased. to evaluate and compare the performance of icce with cce, chimp optimization, black widow optimization, political optimizer, barnacles mating optimizer, snake optimizer, and aquila optimizer, we implement them on 29 test functions from 2017 ieee congress on evolutionary computation cec 2017 . the results of friedman mean rank and wilcoxon signed-rank tests confirm the higher performance of icce compared to other algorithms.