A multiobjective and interactive genetic algorithm to optimize the building form in early design stages

As simulation researchers in the field of performance- driven architecture, we mainly describe in this paper an interactive genetic algorithm (IGA) especially developed for eco-performance and real-time creative design simulations, associated with a simple and intuitive human machine interface. It has been originally created during the french ANR project EcCoGen dealing with creativity assistance, with the objective of "reducing the gap" between architectural design and current scientific knowledge needed to optimize the building form in early design stage, reduce its energy consumption and bring a real help to the architect's decisions

[1]  Andrzej Jaszkiewicz,et al.  Genetic local search for multi-objective combinatorial optimization , 2022 .

[2]  S. Louis,et al.  Reducing User Fatigue in Interactive Genetic Algorithms by Evaluation of Population Subsets , 2009 .

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  Daniel Angus,et al.  Multiple objective ant colony optimisation , 2009, Swarm Intelligence.

[5]  Denis Kelliher,et al.  ArDOT: a tool to optimise environmental design of buildings , 2003 .

[6]  Hisao Ishibuchi,et al.  Balance Between Genetic Search And Local Search In Hybrid Evolutionary Multi-criterion Optimization Algorithms , 2002, GECCO.

[7]  Hervé Lequay,et al.  Development of polynomial regression models for composite dynamic envelopes’ thermal performance forecasting , 2013 .

[8]  Kalyanmoy Deb,et al.  Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction , 2011, Multi-objective Evolutionary Optimisation for Product Design and Manufacturing.

[9]  R. Cerf Une théorie asymptotique des algorithmes génétiques , 1994 .

[10]  Sushil J. Louis,et al.  A Model of Creative Design Using Collaborative Interactive Genetic Algorithms , 2008 .

[11]  Philippe Clauss,et al.  Efficient Parallel Implementation of Evolutionary Algorithms on GPGPU Cards , 2009, Euro-Par.

[12]  Shady Attia,et al.  Simulation-based decision support tool for early stages of zero-energy building design , 2012 .

[13]  Gary B. Lamont,et al.  Evolutionary algorithms for solving multi-objective problems, Second Edition , 2007, Genetic and evolutionary computation series.

[14]  Hisao Ishibuchi,et al.  Multi-objective genetic local search algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[15]  Tomasz Arciszewski,et al.  EMPIRICAL ANALYSIS OF MEMETIC ALGORITHMS FOR CONCEPTUAL DESIGN OF STEEL STRUCTURAL SYSTEMS IN TALL BUILDINGS , 2006 .

[16]  Grégoire Carpentier,et al.  Approche computationnelle de l'orchestration musciale - Optimisation multicritère sous contraintes de combinaisons instrumentales dans de grandes banques de sons. (Computational Approach of Musical Orchestration - Constrained Multiobjective Optimization in Large Sound Sample Databases) , 2008 .

[17]  Taïcir Loukil,et al.  The Pareto fitness genetic algorithm: Test function study , 2007, Eur. J. Oper. Res..

[18]  Wenjie Yang,et al.  Performance-driven architectural design and optimization technique from a perspective of architects , 2013 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Fearghal Morgan,et al.  Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection , 2011, IEEE Transactions on Evolutionary Computation.

[21]  Mohamed Hamdy,et al.  IMPLEMENTATION OF PARETO-ARCHIVE NSGA-II ALGORITHMS TO A NEARLY-ZERO-ENERGY BUILDING OPTIMISATION PROBLEM , 2012 .