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I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi Zhang @Yi-Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi-Zhang @Yi_Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi_Zhang @YiZhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @YiZhang @Yi Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi Zhang @Yi-Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi-Zhang @Yi Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi Zhang Zhang can weigh in on this?

I'll try and give an explanation in simple terms of how the scaling works and what the effect is, following the image in the documentation. Here is the image which illustrates objective scaling.

Some observations:

1. Scaling can help with the efficiency of certain algorithms, though I'm not familiar enough with NSGA2 (the default algorithm in jEPlus+EA) to know if that applies here.

2. Scaling sets any values which are less than the min to be equal to 0. This prevents an algorithm which is trying to minimise the objective function from ranking solutions where the value is less than the minimum as better than one which is at the minimum. This avoids runaway optimisation to unfeasible values.

3. Scaling also sets the max value at 1 x n where n is the the weight applied to that variable. The weighting here means that minimising this objective is n times more influential on the result.

As to why the "Electricity [kWh]" objective is scaled and the "Construction Cost [$/m2]" objective is not in the example file, that is harder to understand. The effect as far as I can see is that the construction cost could potentially be weighted as 1000 times more important by the evolutionary algorithm. Perhaps @Yi Zhang Zhang can weigh in on this?