OPTARCH2019 – Optimization Driven Architectural Design

The purpose of the Research Topic aims to bring together the scientific community working in the broader field of Engineering Optimization and Architectural Design including problems formulation, computational methods and software development. The OPTARCH2019 Research Topic will facilitate the exchange of ideas in topics of mutual interests and will serve as a platform for establishing links between research groups with complementary activities. Thus, the communities of Mathematical Programming and Nature-Inspired Search Algorithms will become more familiar with important application areas arising from Architectural design real-world problems. Moreover, the Architectural Design Community will be exposed to advanced numerical methods and software tools, which can highly assist in tackling combinatorial optimization problems.

The topics of the OPTARCH2019 Research Topic include, but are not limited to the following areas:

 Shape and topology optimization: topological derivatives, level set methods, isogeometry-based shape optimization, topometry and topography optimization, sensitivity analysis, etc.
 Linear, nonlinear, stochastic, parametric, discrete and dynamic programming – modelling.
 Optimization software.
 Evolutionary algorithms, swarm optimization, scatter search, etc.
 Parallel and distributed computing in optimization – Cloud computing, GPGPU computing environment.
 Theory of metaheuristics, landscape analysis, convergence, large neighborhoods, etc.
 Multiple criteria decision making and optimization.
 Local search, tabu search, simulated annealing, VNS, ILS, etc.
 Hybrid methods with metaheuristics, machine learning, game theory, mathematical programming, constraint programming, co-evolutionary, etc.
 Energy (energy harvesting, storage, delivery, efficiency, etc.), environment and climate.
 Artificial intelligence, surrogates-metamodels, fuzzy systems.
 Structural optimization: composite materials, transient loads and crash, fatigue and fracture, etc.
 Lifecycle design optimization and structural health monitoring.
 Multidisciplinary and multiphysics design optimization.
 Emergent nature inspired algorithms: quantum computing, artificial immune systems, DNA computing, etc.
 Design under uncertainty: robust design optimization, reliability based design optimization, theoretical foundations and framework.
 Optimization in emerging areas like micro- and nano-mechanics, multiscale, additive manufacturing.
 Generative and parametic design.