FA23_CEE434_HW4

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Jan 9, 2024

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CEE 434 Environmental Systems I, Fall 2023 Homework #4 (GA, Reservoir Modeling) Due Nov. 19 th Problem 1 (60 points). Recall the water allocation example that we discussed in the class (GA Lecture). The problem is to determine the optimal values of diversions to the three firms located along the river to maximize the total net benefits obtained from the firms. The total amount of water available for the firms is limited to 50% of the river flow in the upstream (Q). i) (10 points) Formulate the optimization problem. ii) (20 points) Assume that ࠵? = 16 . Use the MATLAB function and script provided for that example and solve the optimization problem using the GA parameters in the table below (do not change other parameters in the options). For each case presented in the table below, run the GA three times and complete the solution tables (see next page). N (Population Size) G (generations) Crossover Fraction Case 1 4 5 0 Case 2 4 10 0 Case 3 4 10 0.20 Case 4 4 10 0.80 Case 5 10 50 0.20 Case 6 10 50 0.80 Case 7 20 100 0.20 Case 8 20 100 0.80 iii) (10 points) Discuss the impact of GA parameters (N, G, Crossover fraction) iv) (10 points) Based on your results, pick the best combination of GA parameters, and solve the optimization problem for ࠵? = 8 , and ࠵? = 32 . Discuss the impact of river flow constraint on each firm’s net benefit and total net benefit. v) (5 points) Assume that you are NOT able to insert the river flow constraint into GA formulation in MATLAB. Suggest another way that you can account for the constraint in GA optimization.
Problem I i NB M 6x_x 2 N13242 7 2 15 22 NB 8 3 05 2 objective function max È N Bi Xi constraints xtxvtx.CO5Q ii see below iii The optimization solution becomes more converged as N and G increase Excessive crossover fraction maydisrupt potential good solutions before they dominate the population while a too small crossover fraction can result in the loss of valuable solutions iv N 50 G 100 crossover fraction o 2 Q 8 254102 25.4105 05017 0.6035 28958 25.4102 25.4097 4106 o.mg 29685 v5.4094 25.4016 0.3964 00481 29564 4073 5.4073 a 2 49.1607 49.1607 3 8 49.1607 49.1607 3 2.33 8 49.1607 49.1607 233 8 49.1607 49.1607 Qj the influence of 1 unit of flow constraint on net benefit and total benefit l
u vi The goal of GA is to reach the optimal solution through iterative process while NLP directly solves problems DP aims to find the best solution at each stage b GA can handle complex large scale non linear systems more efficiently and with less complexity although it may sacrifice some accuracy NLP hrs high precision but involves complex calculations is easily calculated manually but for super large systems it becomes intricate and generates excessive data
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