en عمومى General پژوهشي Research <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black">We utilized the most applicable artificial intelligence systems for the challenge of cooling load (CL) in housing units. We fine-tuned them in 2 stages using an innovative evolutionary algorithm called Biogeography-Based Optimization (BBO). The abovementioned procedure is then applied to establish a connection between the system&#39;s input and output characteristics. The vital output of the system was the measure of CL. In contrast, the input attributes included surface area, relative compactness, roof area, wall area, glazing area distribution, overall height, and orientation. Two well-known statistical indices, the correlation coefficient (</span></span></span></span> <span style="font-size:10.0pt"><span cambria="" math="" style="font-family:"><m:ctrlpr></m:ctrlpr></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>R</m:r></span></span></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>2</m:r></span></span></span></span> <span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black"><span style="position:relative"><span style="top:8.5pt"><img alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAYCAIAAABFpVsAAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAKNJREFUOE/tkkESwyAIRbHHCb2OyXECx6lex5zHiNZUnJh20UUX/RtnBD4PBhNjhM90u07zs8mafcpLrkM5ixRS1FmwLoK8SiWq5Ej+xDUQSlWWFOrkFC3BnnVCbHtsfF/g4SbmBKtdpcHLtGHLrAXgqRPQg7oCZJxAQAtvo/1pAGlZJ+z30I1lV0LPA+NjrxWzzHJibb52A+2Eb87ln/pTG9gBcCno2kWBmEEAAAAASUVORK5CYII=" style="width:14px; height:24px" ></span></span></span></span></span></span><span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black">) and root mean squared error (RMSE) were used to assess the BBO approach&rsquo;s expected outcome for data sets. According to the findings of the BBO network&#39;s initial stage, the </span></span></span></span> <span style="font-size:10.0pt"><span cambria="" math="" style="font-family:"><m:ctrlpr></m:ctrlpr></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>R</m:r></span></span></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>2</m:r></span></span></span></span> <span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black"><span style="position:relative"><span style="top:8.5pt"><img alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAYCAIAAABFpVsAAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAKNJREFUOE/tkkESwyAIRbHHCb2OyXECx6lex5zHiNZUnJh20UUX/RtnBD4PBhNjhM90u07zs8mafcpLrkM5ixRS1FmwLoK8SiWq5Ej+xDUQSlWWFOrkFC3BnnVCbHtsfF/g4SbmBKtdpcHLtGHLrAXgqRPQg7oCZJxAQAtvo/1pAGlZJ+z30I1lV0LPA+NjrxWzzHJibb52A+2Eb87ln/pTG9gBcCno2kWBmEEAAAAASUVORK5CYII=" style="width:14px; height:24px" ></span></span></span></span></span></span><span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black">&nbsp;and RMSE amounts for the training and testing data sets were 0.965281 and 0.06773, respectively. Per the </span></span></span></span> <span style="font-size:10.0pt"><span cambria="" math="" style="font-family:"><m:ctrlpr></m:ctrlpr></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>R</m:r></span></span></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>2</m:r></span></span></span></span> <i><span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r> </m:r></span></span></span></span></i> <span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black"><span style="position:relative"><span style="top:8.5pt"><img alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABEAAAAYCAIAAACTAIFlAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAKxJREFUOE/tUsERwyAMMx0n7jpOxsGMU1gH5qEQmgRTEq53ffWqJ5YtS1jFGOFD3K75blYrZlfzks4pLCH7VLUEZA8W5AeBQhOwLN4gFT3jPidPkF2pWoskfutnQqxVg7kv8LCTMbUhqZMlD5lqbemn7PZCx8x7Qttu6xzPwIsJo+8SflBrCixW7/U3GZBmdGYktf/PZqUYb+IVptT3760bx+BG/z0nR/RruT0BNpYAVvHFMxAAAAAASUVORK5CYII=" style="width:17px; height:24px" ></span></span></span></span></span></span><span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black">and RMSE, the testing data set, and suggested BBO-MLP forecasting network models acquired amounts of 0.96007 and 0.06946, respectively. In the second stage, data for ten distinct alpha values are achieved. These data suggest that an alpha of 1.1 provides excellent efficiency. In addition, the amounts of </span></span></span></span> <span style="font-size:10.0pt"><span cambria="" math="" style="font-family:"><m:ctrlpr></m:ctrlpr></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>R</m:r></span></span></span></span> <span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span cambria="" math="" style="font-family:"><span style="color:black"><m:r><m:rpr><m:scr m:val="roman"><m:sty m:val="p"></m:sty></m:scr></m:rpr>2</m:r></span></span></span></span> <span lang="EN-US" style="font-size:12.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black"><span style="position:relative"><span style="top:8.5pt"><img alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAYCAIAAABFpVsAAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAAKNJREFUOE/tkkESwyAIRbHHCb2OyXECx6lex5zHiNZUnJh20UUX/RtnBD4PBhNjhM90u07zs8mafcpLrkM5ixRS1FmwLoK8SiWq5Ej+xDUQSlWWFOrkFC3BnnVCbHtsfF/g4SbmBKtdpcHLtGHLrAXgqRPQg7oCZJxAQAtvo/1pAGlZJ+z30I1lV0LPA+NjrxWzzHJibb52A+2Eb87ln/pTG9gBcCno2kWBmEEAAAAASUVORK5CYII=" style="width:14px; height:24px" ></span></span></span></span></span></span><span lang="EN-US" style="font-size:10.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times=""><span style="color:black">&nbsp;and RMSE for the testing data set is (0.95113 and 0.07667) and (0.95574 and 0.07628) for the training data set, respectively. </span></span></span></span> Cooling-load, Residential buildings, Computational Intelligence, BBO, Hybrid 61 79 http://aisesjournal.com/browse.php?a_code=A-10-144-1&slc_lang=en&sid=1 Ravinder Kumar rav.chauhan@yahoo.co.in 1003194753284600190 1003194753284600190 Yes Director Research, Karnavati University, Gandhinagar-382422, Gujarat, India Hanumant Jagtap jagtaphp@gmail.com 1003194753284600191 1003194753284600191 No Assistant Professor, Zeal College of Engineering and Research Narhe, Pune-411041, Maharashtra, India