A Multi-Objective Genetic Algorithm Framework for Earlier Phases of Architectural Design - A Case Study

Projeto paramétrico orientado ao desempenho de elementos de fachada

“…O Galapagos contém dois métodos de otimização determinísticos -um algoritmo genético e um algoritmo de recozimento simulado (Simulated Annealing) -baseados num único objetivo (Rutten, 2013). Múltiplos objetivos também podem ser explorados no Galapagos por soma ponderada (Kocabay;Alaçam, 2017). O Octopus é um resolvedor evolutivo com dois métodos de otimização multiobjetiva -o HypE (Hypervolume-based Many-Objective Optimization) e o SPEA-II (Strength Pareto Evolutionary Algorithm II) (Vierlinger, 2021).…”

Dynamic Anthropometric Modeling Interface

“…The optimization plug-in used in this paper is Octopus for Grasshopper (Vierlinger 2018;Rutten 2020). This multi-objective solver has been used to create a "heuristic optimization workflow" for interactively exploring different building geometries (Ashour and Kolarevic 2015), and to implement a genetic algorithm to assist architects in exterior designs (Kocabay and Alaçam 2017). While these two examples focused on the building and urban scale, Octopus has also been used at the interior level, which is where DAMI will be implemented.…”

Developing a Multi-Objective Sustainable Supply Chain Network Location-Allocation Model for Perishable Products

“…Schaffer (1985) has proposed the first multi-objective GA, called vector evaluated GA (or VEGA). Niched Pareto Genetic Algorithm (NPGA), Subsequently, Horn et al (1994) developed several multi-objective evolutionary algorithms, including the multi-objective Genetic Algorithm (MOGA) Kocabay and Alaçam (2017) the Weight Based Genetic Algorithm (WBGA) Hajela and Lin (1992) Random Weighted Genetic Algorithm (RWGA) Konak et al (2006) Non-dominated Sorting Genetic Algorithm (NSGA) (Konak et al, 2006). Particle Swarm Optimization (PSO) Patidar et al (2018), and Simulated Annealing (SA) Chalmardi et al (2019), Eskandari-Khanghahi et al (2018) ε-constraint technique, is also used by researchers Arvan et al (2015), Rohmer et al (2019) Some of them devised a solution strategy to solve their model, based on Lagrangian relaxation and ε-constraint Diabat et al (2019) Some models Isaloo and Paydar (2020), Yakavenka et al (2020) Production and distribution planning are the two main optimization problems in the supply chain and their decisions are often made separately.…”