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These are the user uploaded subtitles that are being translated: 1 00:00:00.05 --> 00:00:03.04 - [Instructor] When creating a new generative design study, 2 00:00:03.04 --> 00:00:05.08 we need to specify a method type 3 00:00:05.08 --> 00:00:08.03 which determines how the study will run. 4 00:00:08.03 --> 00:00:12.03 This can be either optimize, randomize, cross-product 5 00:00:12.03 --> 00:00:13.06 or like this. 6 00:00:13.06 --> 00:00:15.04 So in this lesson, let's take a look 7 00:00:15.04 --> 00:00:17.00 at what each of these mean, 8 00:00:17.00 --> 00:00:20.09 and how they can influence the generative design process. 9 00:00:20.09 --> 00:00:23.00 Let's start with optimize. 10 00:00:23.00 --> 00:00:25.05 The optimize method allows us to generate 11 00:00:25.05 --> 00:00:28.06 and evolve a multitude of design options 12 00:00:28.06 --> 00:00:31.02 to improve a solution based on a single 13 00:00:31.02 --> 00:00:34.01 or multiple objective design goals. 14 00:00:34.01 --> 00:00:36.09 In doing so, the optimization method will aim 15 00:00:36.09 --> 00:00:41.01 to either minimize or maximize some design criteria 16 00:00:41.01 --> 00:00:44.09 by tweaking the parameters that drive the design. 17 00:00:44.09 --> 00:00:48.01 For example, say that we wanted to optimize the amount 18 00:00:48.01 --> 00:00:50.02 of twist in this tower, 19 00:00:50.02 --> 00:00:54.01 to maximize the amount of sunlight entering the North face. 20 00:00:54.01 --> 00:00:55.09 There are three things to consider 21 00:00:55.09 --> 00:00:58.01 with an optimization study. 22 00:00:58.01 --> 00:01:00.08 Firstly, what are the variables that can be adjusted 23 00:01:00.08 --> 00:01:02.01 by the algorithm? 24 00:01:02.01 --> 00:01:05.00 In this example, the variables are the rotation 25 00:01:05.00 --> 00:01:06.01 of the tower. 26 00:01:06.01 --> 00:01:08.00 However, this could be many variables 27 00:01:08.00 --> 00:01:12.00 that impact the design, such as height, width, or length. 28 00:01:12.00 --> 00:01:14.08 It really depends on what we're trying to achieve. 29 00:01:14.08 --> 00:01:17.05 Secondly, we need to set some constraints 30 00:01:17.05 --> 00:01:20.04 so that the algorithm does not test scenarios 31 00:01:20.04 --> 00:01:23.05 that we know are not achievable or desirable. 32 00:01:23.05 --> 00:01:25.03 This reduces the search space 33 00:01:25.03 --> 00:01:27.08 for the algorithm giving it a better chance 34 00:01:27.08 --> 00:01:30.00 at finding an optimal solution. 35 00:01:30.00 --> 00:01:33.02 For example, with our tower, we may constrain the amount 36 00:01:33.02 --> 00:01:36.07 of twist to between 0 and 90 degrees, 37 00:01:36.07 --> 00:01:40.01 as any more than this would result in an undesirable design. 38 00:01:40.01 --> 00:01:43.02 Lastly, we need to define the objectives 39 00:01:43.02 --> 00:01:46.06 or goals of the optimization study. 40 00:01:46.06 --> 00:01:49.08 This allows the algorithm to score 41 00:01:49.08 --> 00:01:53.04 or rate the quality of life each generated solution. 42 00:01:53.04 --> 00:01:57.00 In our example, it is to maximize the amount of sunlight. 43 00:01:57.00 --> 00:01:59.04 However, this could be multiple objectives, 44 00:01:59.04 --> 00:02:01.08 such as maximizing the amount of sunlight 45 00:02:01.08 --> 00:02:05.06 while also trying to minimize the amount of cost. 46 00:02:05.06 --> 00:02:09.02 Once the optimization study has scored each design 47 00:02:09.02 --> 00:02:12.00 in the initial generation, or run, 48 00:02:12.00 --> 00:02:14.05 it will select the best performing solutions 49 00:02:14.05 --> 00:02:16.01 and then use their parameters 50 00:02:16.01 --> 00:02:19.01 to generate the next generation of solutions. 51 00:02:19.01 --> 00:02:22.02 Over time these optimizations studies improve 52 00:02:22.02 --> 00:02:25.08 by getting closer and closer to an optimal goal. 53 00:02:25.08 --> 00:02:28.04 So this method is useful when we want the study 54 00:02:28.04 --> 00:02:32.05 to improve a solution based on some scoring metric. 55 00:02:32.05 --> 00:02:34.05 Next is the randomized method. 56 00:02:34.05 --> 00:02:36.09 This study method is a little bit simpler 57 00:02:36.09 --> 00:02:38.09 as the goals don't need to be set. 58 00:02:38.09 --> 00:02:42.06 We simply set the variables that are constrained 59 00:02:42.06 --> 00:02:45.00 and then the study method will generate a number 60 00:02:45.00 --> 00:02:48.05 of random solutions within those bounds. 61 00:02:48.05 --> 00:02:50.08 The results can then be searched through. 62 00:02:50.08 --> 00:02:54.06 This method is useful if we don't have a specific goal 63 00:02:54.06 --> 00:02:56.01 that we want to optimize, 64 00:02:56.01 --> 00:02:58.03 and simply want to search a wide variety 65 00:02:58.03 --> 00:03:00.04 of potential solutions. 66 00:03:00.04 --> 00:03:02.08 Cross product is similar to randomize 67 00:03:02.08 --> 00:03:04.06 in that we don't set the goal, 68 00:03:04.06 --> 00:03:07.00 we just set the constrained variables 69 00:03:07.00 --> 00:03:10.09 for the algorithm to then generate every solution possible 70 00:03:10.09 --> 00:03:14.04 within those bounds at specific intervals. 71 00:03:14.04 --> 00:03:17.07 So for example, say we wanted to test the twist 72 00:03:17.07 --> 00:03:21.01 in our tower, but also controlling the width 73 00:03:21.01 --> 00:03:25.00 with the bounds of say 0 to 90 degrees for the twist 74 00:03:25.00 --> 00:03:27.07 and 10 to 20 meters for the width. 75 00:03:27.07 --> 00:03:30.07 With cross product, we specify the number 76 00:03:30.07 --> 00:03:35.01 of values to test with each variable within those bounds. 77 00:03:35.01 --> 00:03:38.04 In this example, it's five values for the twist 78 00:03:38.04 --> 00:03:40.07 and five values for the width. 79 00:03:40.07 --> 00:03:42.07 The algorithm will take the values 80 00:03:42.07 --> 00:03:46.06 of each variable evenly spaced by the number that we specify 81 00:03:46.06 --> 00:03:49.07 and test it against every other value specified 82 00:03:49.07 --> 00:03:54.00 for the other variables, creating a matrix of solutions. 83 00:03:54.00 --> 00:03:57.02 This is also useful like the randomized method 84 00:03:57.02 --> 00:04:00.02 to search through a wide variety of solutions 85 00:04:00.02 --> 00:04:02.08 with no particular goal to start with. 86 00:04:02.08 --> 00:04:05.03 Lastly, the like this method allows us 87 00:04:05.03 --> 00:04:07.08 to finally tune a design option 88 00:04:07.08 --> 00:04:10.00 that we've already arrived at. 89 00:04:10.00 --> 00:04:12.09 We do this by first specifying the variables 90 00:04:12.09 --> 00:04:14.06 that the algorithm can change, 91 00:04:14.06 --> 00:04:17.06 such as the amount of twist and width. 92 00:04:17.06 --> 00:04:20.01 The like this method will then generate a number 93 00:04:20.01 --> 00:04:23.07 of random solutions by adjusting these variables 94 00:04:23.07 --> 00:04:27.05 within 20% of the values that we set. 95 00:04:27.05 --> 00:04:29.04 So the end result will be a number 96 00:04:29.04 --> 00:04:33.01 of potential design solutions close to the variables 97 00:04:33.01 --> 00:04:35.01 that we have already determined. 98 00:04:35.01 --> 00:04:38.07 This is useful for fine tuning our design solutions, 99 00:04:38.07 --> 00:04:41.00 so best used when we're already close 100 00:04:41.00 --> 00:04:43.05 to the variables that we know that we want. 101 00:04:43.05 --> 00:04:46.04 Now, that we're up to speed with the different method types, 102 00:04:46.04 --> 00:04:49.05 let's go ahead and optimize the desk study 103 00:04:49.05 --> 00:04:50.07 from the previous lesson. 8489