Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
The Unified Modeling Language user guide
The Unified Modeling Language user guide
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
Artificial Intelligence Review
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Advanced Engineering Informatics
Computer assistance for sustainable building design
EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
Multicriteria optimization of paneled building envelopes using ant colony optimization
EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
Advances in Engineering Software
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Simulation-based optimization can assist green building design by overcoming the drawbacks of trial-and-error with simulation alone. This paper presents an object-oriented framework that addresses many particular characteristics of green building design optimization problems such as hierarchical variables and the coupling with simulation programs. The framework facilitates the reuse of code and can be easily adapted to solve other similar optimization problems. Variable types supported include continuous variables, discrete variables, and structured variables, which act as switches to control a number of sub-level variables. The framework implements genetic algorithms to solve (1) unconstrained and constrained single objective optimization problems, and (2) unconstrained multi-objective optimization problems. The application of this framework is demonstrated through a case study which uses a multi-objective genetic algorithm to explore the trade-off relationship between life-cycle cost and life-cycle environmental impacts for a green building design.