اراﺋﻪ روش ﺗﺮﮐﯿﺒﯽ ﻧﻮﯾﻦ DSM ﺟﻬﺖ ﺗﻨﻈﯿﻢ ﭘﺎراﻣﺘﺮ اﻟﮕﻮرﯾﺘﻢﻫﺎي ﻓﺮااﺑﺘﮑﺎري

A new hybrid method DSM for parameter setting of meta-heuristic algorithms

ﺗﻨﻈﯿﻢ ﭘﺎراﻣﺘﺮﻫﺎي اﻟﮕﻮرﯾﺘﻢﻫﺎي ﻓﺮااﺑﺘﮑﺎري در ﻋﻤﻠﮑﺮد آﻧﻬﺎ ﺑﺴﯿﺎر ﻣﺆﺛﺮ ﻣﯽﺑﺎﺷﺪ و ﻣﻌﻤﻮﻻً ﺑﻪﺻﻮرت ﺗﺠﺮﺑﯽ اﻧﺠﺎم ﻣﯽﺷﻮد ﮐﻪ ﺑﺴﯿﺎر زﻣﺎن ﺑﺮ اﺳﺖ. در اﯾﻦ ﭘﮋوﻫﺶ ﯾﮏ روش ﺗﺮﮐﯿﺒﯽ ﺟﻬﺖ اﻧﺘﺨﺎب ﭘﺎراﻣﺘﺮﻫﺎي ﺑﻬﯿﻨﻪ اﻟﮕﻮرﯾﺘﻢﻫﺎي ﻓﺮااﺑﺘﮑﺎري اراﯾﻪ ﺷﺪه اﺳﺖ. روش ﭘﯿﺸﻨﻬﺎدي ﺗﺮﮐﯿﺒﯽ از روشﻫﺎي ﺗﺤﻠﯿﻞ ﭘﻮﺷﺸﯽ دادهﻫﺎ و ﺳﻄﺢ ﭘﺎﺳﺦ ﻣﯽﺑﺎﺷﺪ و DSM ﻧﺎﻣﯿﺪه ﻣﯽﺷﻮد. در واﻗﻊ اﯾﻦ روش ﻗﺎﺑﻞ اﺳﺘﻔﺎده ﺑﺮاي ﺑﻬﯿﻨﻪﺳﺎزي ﻣﺴﺎﺋﻞ ﭼﻨﺪ ﻫﺪﻓﻪ ﻣﯽﺑﺎﺷﺪ و ﻣﺰﯾﺖ اﺻﻠﯽ آن اﯾﺠﺎد و ﺑﻬﯿﻨﻪﺳﺎزي ﯾﮏ روﯾﻪي ﭘﺎﺳﺦ ﮐﺎراﯾﯽ ﺑﻪ ﺟﺎي ﺑﻬﯿﻨﻪﺳﺎزي ﭼﻨﺪﯾﻦ روﯾﻪ ﭘﺎﺳﺦ ﺧﺮوﺟﯽﻫﺎ ﻣﯽﺑﺎﺷﺪ، ﻫﻤﭽﻨﯿﻦ ﻋﻼوه ﺑﺮ ﺑﻬﯿﻨﻪﺳﺎزي ﭘﺎراﻣﺘﺮﻫﺎ ﺑﻪﺻﻮرت ﻫﻢزﻣﺎن ﺑﻪ ﺑﯿﺸﯿﻨﻪ ﺳﺎزي ﮐﺎراﯾﯽ ﻧﯿﺰ ﻣﯽﭘﺮدازد. در اﯾﻦ ﭘﮋوﻫﺶ از روش ﭘﯿﺸﻨﻬﺎدي DSM ﺟﻬﺖ ﺗﻨﻈﯿﻢ ﭘﺎراﻣﺘﺮﻫﺎي اﻟﮕﻮرﯾﺘﻢ ﺑﻬﯿﻨﻪﺳﺎزي ﻓﺎﺧﺘﻪ ﺑﺮاي ﺑﻬﯿﻨﻪ ﺳﺎزي ﺗﻮاﺑﻊ اﺳﺘﺎﻧﺪارد و آزﻣﺎﯾﺸﯽ آﮐﻠﯽ و راﺳﺘﺮﯾﮕﯿﻦ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. در روش ﺗﺮﮐﯿﺒﯽ DSM، اﺑﺘﺪا ﻣﻘﺪار ﮐﺎراﯾﯽ ﺑﺎ اﺳﺘﻔﺎده از ﺗﺤﻠﯿﻞ ﭘﻮﺷﺸﯽ دادهﻫﺎ ﺑﺮاي ﻫﺮ ﻣﺠﻤﻮﻋﻪ از ﭘﺎراﻣﺘﺮﻫﺎي اﻟﮕﻮرﯾﺘﻢ ﻓﺮااﺑﺘﮑﺎري ﻣﺤﺎﺳﺒﻪ ﻣﯽﮔﺮدد، ﺳﭙﺲ روﯾﻪ ﭘﺎﺳﺦ ﺑﺮاي ﮐﺎراﯾﯽ ﺑﺮ ﺣﺴﺐ ﭘﺎراﻣﺘﺮﻫﺎي اﻟﮕﻮرﯾﺘﻢ ﻓﺮااﺑﺘﮑﺎري ﺑﺎ اﺳﺘﻔﺎده از روش ﺳﻄﺢ ﭘﺎﺳﺦ ﺗﻌﯿﯿﻦ ﻣﯽﮔﺮدد. در ﻧﻬﺎﯾﺖ ﺑﺎ ﺑﻬﯿﻨﻪﺳﺎزي روﯾﻪ ﮐﺎراﯾﯽ ﻣﻘﺎدﯾﺮ ﺑﻬﯿﻨﻪ ﭘﺎراﻣﺘﺮﻫﺎي اﻟﮕﻮرﯾﺘﻢ ﻓﺎﺧﺘﻪ ﺑﺪﺳﺖ ﻣﯽآﯾﺪ. ﺑﻪ ﻣﻨﻈﻮر اﻋﺘﺒﺎرﺳﻨﺠﯽ ﻧﺘﺎﯾﺞ ﺣﺎﺻﻠﻪ از روش ﭘﯿﺸﻨﻬﺎدي ﺑﺎ روش ﻣﺸﺎﺑﻪ ﻣﻘﺎﯾﺴﻪ ﮔﺮدﯾﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﻧﺸﺎن دﻫﻨﺪه ﻋﻤﻠﮑﺮد ﺑﻬﺘﺮ اﻟﮕﻮرﯾﺘﻢ ﻓﺮاﺑﺘﮑﺎري ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ زﻣﺎن ﺣﻞ، ﺗﻌﺪاد ﺗﮑﺮارﻫﺎ و دﻗﺖ ﺗﺎﺑﻊ ﺑﻬﯿﻨﻪ ﺳﺎزي ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﯾﺮ روش ﻫﺎي ﻣﺸﺎﺑﻪ اﺳﺖ.

Parameters of meta-heuristic algorithms are very effective in their performance and are usually done experimentally, which is very time-consuming. In this research, a hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis methods and response surface methodology and is called DSM. In fact, this method can be used to optimize multi-objective problems and its main advantage is to create and optimize one performance response procedure instead of optimizing multiple output response procedures. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the proposed DSM method has been used to adjust the parameters of the cuckoo optimization algorithm to optimize the standard and experimental Aklay and Rastrigin functions. In the hybrid DSM method, first, the efficiency value is calculated using data envelopment analysis for each set of meta-heuristic algorithm parameters, then the response procedure for performance is determined according to the meta-heuristic algorithm parameters using the response surface methodology. Finally, by optimizing the efficiency surface, the optimal values of the cuckoo algorithm parameters are obtained. In order to validate, the results of the proposed method have been compared with a similar method. The results show better performance of the hybrid algorithm in terms of solution time, number of iterations, and accuracy of the optimization function compared to other similar methods.