Minimizing Single-Family Homes’ Carbon Dioxide Emissions and Life Cycle Costs: An Improved Billiard-Based Optimization Algorithm Approach
Bibliographic record
Abstract
In recent years, research has focused on designing buildings with higher energy efficiency and lower emissions by considering multiple objectives. This can impact financial savings, smaller environmental footprints, and energy consumption optimization. The purpose of the current study is to develop a new technique to solve this challenging multiple-objective optimization problem. While there are different methods to solve optimization problems, based on the NLP theory, there is not any metaheuristic algorithm that can solve all the problems accurately. Sometimes, the outcome of a basic algorithm is a local optimum. Therefore, to reach the global optimum, we propose the Improved Billiard-based Optimization Algorithm (IBOA). Moreover, in some cases, the basic model suffers from premature convergence, which prevents reaching an accurate result. Hence, this study aims to solve this problem and attain better convergence results using the proposed method to minimize CO2-eq emissions and life cycle costs. The design variables include some parameters of the envelope of a single-family residential dwelling to indicate the efficiency of the presented method. Based on the Pareto optimum solutions achieved, it is proved that the method is effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".