Project

Optimus: Metaheuristic Optimization for Grasshopper 3d

Goal: Optimus is a plug-in for Grasshopper 3d algorithmic modeling environment to use in architectural design optimization and building performance optimization. The first release of Optimus is employing "Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies". In this way, Optimus can not only adapt Crossover and Mutation rates for different problems but also empowers the algorithm while searching for near-optimal solutions. Optimus v1.0.0 (beta) is tested with several benchmark problems and with a design optimization problem. Comparing with other existing single-objective optimization algorithms available for Grasshopper 3d, Optimus presented much better results.

Updates
0 new
6
Recommendations
0 new
4
Followers
0 new
24
Reads
6 new
373

Project log

Berk Ekici
added an update
Today, we released the new version of Optimus in Food4Rhino with Cemre çubukçuoğlu , Mehmet Fatih Tasgetiren ,and Sevil Sariyildiz . We added a new component to handle single objective constrained optimization problems called Near Feasibility Threshold (NFT). NFT module can also be used with other optimizers in Grasshopper 3D. Besides, various hyperparameters for self-adaptive strategy and different boundary assignment methods can also be used. To check the new release please visit https://www.food4rhino.com/app/optimus.
 
Berk Ekici
added an update
Since the first beta release in April 2019, Optimus has reached more than 1000 downloads on https://www.food4rhino.com/app/optimus. As Optimus team, we would like to thank you for your interest in our plug-in!
 
Cemre çubukçuoğlu
added 2 research items
Architectural design is a process that considers many objectives to satisfy. In general, these objectives are conflicting with each other. On the other hand, many design parameters are associated with these conflicting objectives, too. Therefore, architectural design is described as a complex task. To handle the complexity, computational optimization methods can be employed to investigate architectural design process in detail. This paper focuses on investigating Pareto-front solutions for theatre hall design using multi-objective evolutionary algorithms. To formulate the theatre hall acoustic design problem, we consider three objectives. Two objectives are minimization of both reverberation time, and total initial cost whereas the third objective is the maximization of seating capacity. In addition, several designs and acoustical performance constraints are defined. To tackle this problem, a multi-objective self-adaptive differential evolution algorithm (JDEMO) is proposed and compared with a well-known non-dominated sorting genetic algorithm-II (NSGA-II) from the literature. Computational results show that the proposed JDEMO algorithm achieves competitive results when compared to the NSGA-II.
Floating neighborhoods are innovative and promising urban areas for challenges in the development of cities and settlements. However, this design task requires a lot of considerations and technical challenges. Computational tools and methods can be beneficial to tackle the complexity of floating neighborhood design. This paper considers the design of a self-sufficient floating neighborhood by using computational intelligence techniques. In this respect, we consider a design problem for locating each neighborhood function in each cluster with a certain density within a floating neighborhood. In order to develop a self-sufficient floating neighborhood, we propose multi-objective evolutionary algorithms, namely, a self-adaptive real-coded genetic algorithm (CGA) as well as a self-adaptive real-coded genetic algorithm (CGA_DE) employing mutation operator of differential evolution algorithm. The only difference between CGA and CGA_DE is the fact that CGA uses random immigration of certain individuals into the population as a mutation operator whereas in the mutation phase of CGA_DE algorithm, the traditional mutation operator DE/rand/1/bin of DE algorithms. The arrangement of individual functions to develop each neighborhood function is further elaborated and formed by using Voronoi diagram algorithm. An application to design a self-sufficient floating neighborhood in Urla district, which is on the west coast of Turkey, İzmir, is presented.
Berk Ekici
added an update
Optimus v1.0.0 (beta) can be downloaded via the link below:
 
Berk Ekici
added a research item
Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover, Optimus facilitates implementing different type of algorithms due to its modular system.
Berk Ekici
added a project goal
Optimus is a plug-in for Grasshopper 3d algorithmic modeling environment to use in architectural design optimization and building performance optimization. The first release of Optimus is employing "Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies". In this way, Optimus can not only adapt Crossover and Mutation rates for different problems but also empowers the algorithm while searching for near-optimal solutions. Optimus v1.0.0 (beta) is tested with several benchmark problems and with a design optimization problem. Comparing with other existing single-objective optimization algorithms available for Grasshopper 3d, Optimus presented much better results.