TY - JOUR
T1 - OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling
AU - Çubukçuoglu, Cemre
AU - Ekici, Berk
AU - Tasgetiren, M. Fatih
AU - Sariyildiz, Sevil
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - grasshopper
KW - optimization
KW - differential evolution
KW - architectural design
KW - computational design
KW - performance based design
KW - building performance optimization
KW - single-objective optimization
KW - architectural design optimization
KW - parametric design
KW - Grasshopper
KW - Architectural design
KW - Parametric design
KW - Building performance optimization
KW - Optimization
KW - Single-objective optimization
KW - Performance based design
KW - Architectural design optimization
KW - Computational design
KW - Differential evolution
UR - http://www.scopus.com/inward/record.url?scp=85075565129&partnerID=8YFLogxK
U2 - 10.3390/a12070141
DO - 10.3390/a12070141
M3 - Article
VL - 12
JO - Algorithms
JF - Algorithms
SN - 1999-4893
IS - 7
M1 - 141
ER -