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Architects face the challenge of exploring various design solutions in the early design stage, often with conflicting optimization goals. To tackle this complexity, they need to rely on tools and methodologies during the conceptual phase to assess and optimize designs, considering multiple aspects of building performance. Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) offer architects new opportunities to work on complex buildings. These advancements empower designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. Multi-Objective Optimization algorithms are employed to address conflicting objectives in the design process. The GENIUS project introduces an Algorithm-Aided Design workflow that optimizes the building shape and Window-to-Wall Ratio of an office building, considering energy and daylight performance. The integration of BIM software, visual programming tools, and Artificial Intelligence techniques (Genetic Algorithms and RBFOpt model-based optimization) allows architects to identify optimal solutions aligning with design objectives. The workflow was validated through a case study of a large office building, focusing on maximizing daylight performance using the Spatial Daylight Autonomy metric and minimizing energy consumption using the Energy Use Intensity metric. The GENIUS project equips architects with a methodology and toolset to improve their designs and identify optimal solutions for complex design challenges. [ABSTRACT FROM AUTHOR]

The objective of this paper is to focus on the technical issues of single-stage thermo-electric coolers (TECs) and two-stage TECs and then apply new methods in optimizing the dimensions of TECs. In detail, some metaheuristics--simulated annealing (SA) and differential evolution (DE)--are applied to search the optimal design parameters of both types of TEC, which yielded cooling rates and coefficients of performance (COPs) individually and simultaneously. The optimization findings obtained by using SA and DE are validated by applying them in some defined test cases taking into consideration non-linear inequality and non-linear equality constraint conditions. The performance of SA and DE are verified after comparing the findings with the ones obtained applying the genetic algorithm (GA) and hybridization technique (HSAGA and HSADE). Mathematical modelling and parameter setting of TEC is combined with SA and DE to find better optimal findings. The work revealed that SA and DE can be applied successfully to solve single-objective and multi-objective TEC optimization problems. In terms of stability, reliability, robustness and computational efficiency, they provide better performance than GA. Multi-objective optimizations considering both objective functions are useful for the designer to find the suitable design parameters of TECs which balance the important roles of cooling rate and COP. [ABSTRACT FROM AUTHOR]