Optimus: self-adaptive differential evolution with ensemble of mutation strategies for grasshopper algorithmic modeling

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dc.authoridMehmet Fatih Taşgetiren / 0000-0002-5716-575X
dc.authorscopusidMehmet Fatih Taşgetiren / 6505799356
dc.authorwosidMehmet Fatih Taşgetiren / CDL-1821-2022
dc.contributor.authorÇubukçuoğlu, Cemre
dc.contributor.authorEkici, Berk
dc.contributor.authorTaşgetiren, Mehmet Fatih
dc.contributor.authorSarıyıldız, Sevil
dc.date.accessioned2020-08-30T20:06:44Z
dc.date.available2020-08-30T20:06:44Z
dc.date.issued2019
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractMost 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.en_US
dc.description.sponsorshipNational Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51435009]en_US
dc.description.sponsorshipM.F.T. was partially funded by the National Natural Science Foundation of China (Grant No. 51435009).en_US
dc.identifier.citationCubukcuoglu, C., Ekici, B., Tasgetiren, M. F., & Sariyildiz, S. (2019). OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling. ALGORITHMS, 12(7). https://doi.org/10.3390/a12070141en_US
dc.identifier.doi10.3390/a12070141en_US
dc.identifier.issn1999-4893en_US
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85075565129en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/a12070141
dc.identifier.urihttps://hdl.handle.net/20.500.12713/608
dc.identifier.volume12en_US
dc.identifier.wosWOS:000478578100002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTaşgetiren, Mehmet Fatihen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofAlgorithmsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGrasshopperen_US
dc.subjectOptimizationen_US
dc.subjectDifferential Evolutionen_US
dc.subjectArchitectural Designen_US
dc.subjectComputational Designen_US
dc.subjectPerformance Based Designen_US
dc.subjectBuilding Performance Optimizationen_US
dc.subjectSingle-Objective Optimizationen_US
dc.subjectArchitectural Design Optimizationen_US
dc.subjectParametric Designen_US
dc.titleOptimus: self-adaptive differential evolution with ensemble of mutation strategies for grasshopper algorithmic modelingen_US
dc.typeArticleen_US

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