{ "article_id": 1278, "public_id": "bac43dee-de5d-4ea2-9a8e-26c18dcdffcc", "internal_identifier": null, "source_identifier": "84:144", "status": "published", "type": "research_article", "title": "MOEA/D Algoritmasının En Önemli İki Ayrıştırma Yöntemi Olan PBI ve TCH’ın Analizi", "title_secondary": null, "abstract": "Günümüzde, birçok araştırmacı optimizasyon problemleri çözen algoritmaları geliştirme eğilimindedir. Gerçek dünya problemleri ise genelde birden çok amaca sahip oldukları için çok amaçlı optimizasyon problemleri üzerine geliştirilen algoritmalar trend olmuştur. Bu çalışmada, çok amaçlı optimizasyon algoritması olan Multi Objective Evulation Algorithm(MOEA/D) incelendi ve algoritma içerisinde yer alan ve genelde amaçların tek bir amaç olarak ifade edilmesini sağlayan en önemli iki metot Tchebycheff (TCH) ve Penalty-based Boundary Intersection (PBI) metotları  iki ve üç amaçlı 11 benchmark(DTLZ ve ZDT ailesi) ile test edildi ve bu alanda en çok kullanılan Hypervolume(HV), Inverted Generation Distance(IGD) ve EPSİLON adında üç adet metrik ile bu sonuçlar kıyaslandı. Bu algoritmanın artıları ve eksileri öne çıkarıldı. Sonuçta MOEA/D hangi yöntem ile kullanılırsa daha verimli olacağı gösterilmiş oldu. Bilim her zaman üzerine koyarak ilerleyen bir gerçeklik olduğu için, bu çalışma MOEA/D üzerine çalışma yapacak araştırmacılara yön gösterici bir çalışma oldu.", "language": "tr", "english_title": "Analysis of PBI and TCH, the Two Most Important Decomposition Methods of the MOEA/D Algorithm", "english_abstract": "Today, many researchers are inclined to develop algorithms that solve optimization problems. Since real-world problems generally have multiple objectives, algorithms developed for multi-objective optimization problems have become a trend. In this study, the Multi-Objective Evolution Algorithm (MOEA/D), a multi-objective optimization algorithm, was examined, and its two most important methods, Tchebycheff (TCH) and Penalty-based Boundary Intersection (PBI), which generally allow objectives to be expressed as a single objective, were tested with 11 benchmarks (DTLZ and ZDT families) for two and three objectives. These results were compared with three metrics most commonly used in this field: Hypervolume (HV), Inverted Generation Distance (IGD), and EPSILON. The advantages and disadvantages of this algorithm were highlighted. Ultimately, it was shown which method would be more efficient when used with MOEA/D. Since science is a constantly evolving reality, this study has served as a guide for researchers who will be working on MOEA/D.", "doi": "10.36287/setsci.4.6.144", "doi_url": "https://doi.org/10.36287/setsci.4.6.144", "resource_url": "https://www.set-science.com/a/bac43dee-de5d-4ea2-9a8e-26c18dcdffcc", "pdf_url": "https://www.set-science.com/a/bac43dee-de5d-4ea2-9a8e-26c18dcdffcc/pdf", "og_image": "https://www.set-science.com/assets/img/og-card-v2.png", "authors": [ { "public_id": "87d6924d-8609-4d88-870e-199d0332232b", "given": "Mustafa", "family": "Altıok", "full": "Mustafa Altıok", "literal": null, "orcid": null, "orcid_url": null, "email": "mustafa.altiok@gop.edu.tr", "corresponding": true, "sequence": "first", "affiliations": [ { "name": "Tokat Gaziosmanpaşa University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] }, { "public_id": "472578d2-bdf1-4cdf-9f4b-f1b26e387897", "given": "Barış", "family": "Koçer", "full": "Barış Koçer", "literal": null, "orcid": null, "orcid_url": null, "email": "mustafa.altiok@gop.edu.tr", "corresponding": false, "sequence": "additional", "affiliations": [ { "name": "Konya Technical University", "department": null, "country": null, "country_code": null, "ror": null, "ror_url": null } ] } ], "keywords": [], "translations": [ { "language": "en", "title": "Analysis of PBI and TCH, the Two Most Important Decomposition Methods of the MOEA/D Algorithm", "abstract": "Today, many researchers are inclined to develop algorithms that solve optimization problems. Since real-world problems generally have multiple objectives, algorithms developed for multi-objective optimization problems have become a trend. In this study, the Multi-Objective Evolution Algorithm (MOEA/D), a multi-objective optimization algorithm, was examined, and its two most important methods, Tchebycheff (TCH) and Penalty-based Boundary Intersection (PBI), which generally allow objectives to be expressed as a single objective, were tested with 11 benchmarks (DTLZ and ZDT families) for two and three objectives. These results were compared with three metrics most commonly used in this field: Hypervolume (HV), Inverted Generation Distance (IGD), and EPSILON. The advantages and disadvantages of this algorithm were highlighted. Ultimately, it was shown which method would be more efficient when used with MOEA/D. Since science is a constantly evolving reality, this study has served as a guide for researchers who will be working on MOEA/D.", "keywords": [ "Optimizasyon, Çok amaçlı optimizasyon, MOEA/D, algoritma,Ayrıştırma yöntemi" ] } ], "subjects": [], "subject_names": [], "published_date": "2019-12-22", "published_year": "2019", "received_date": null, "accepted_date": null, "volume": "9", "issue": null, "page_first": "502", "page_last": "507", "article_number": null, "conference": { "short_name": "ISAS WINTER-2019 (ENS)", "full_title": "4th International Symposium on Innovative Approaches in Engineering and Natural Sciences", "name": "4th International Symposium on Innovative Approaches in Engineering and Natural Sciences", "acronym": "ISAS WINTER-2019 (ENS)", "location": "Samsun, Türkiye", "start_date": "2019-11-22", "end_date": null, "public_id": "ede2435c-3820-4bef-84a9-59e7d8de9d60" }, "proceedings": { "title": "4th International Symposium on Innovative Approaches in Engineering and Natural Sciences", "series_title": "SETSCI Conference Proceedings", "publisher": "SET Technology", "isbn": "978-605-81988-8-3", "eisbn": null, "issn": null, "eissn": "2687-5527", "doi": null, "doi_prefix": "10.36287", "slug": "setsci-volume-9-2019-legacy-84", "public_id": "5d3c128d-5090-429f-8398-4e32c57e5584", "url": "https://www.set-science.com/p/setsci-volume-9-2019-legacy-84" }, "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/", "code": "CC-BY-4.0", "open_access": true }, "copyright": { "holder": "SET Technology", "year": "2019" }, "funding_statement": null, "funders": [], "references": [ { "order": 1, "raw": "A. OZKIS, \"Girdap arama ve yapay alg algoritmalarının çok amaçli optimzasyon problemlerine uyarlanmasi,\" phD, Computer Engineering, Selcuk Universty, KONYA, 2018.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 2, "raw": "A. Özkış and A. Babalık, \"A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm,\" Information Sciences, vol. 402, pp. 124-148, 2017.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 3, "raw": "J. Schaffer, \"Multiple objective optimization with vector evaluated,\" ph.D., Genetic Algorithms, Vanderbilt Universty, 1984.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 4, "raw": "K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, \"A fast and elitist multiobjective genetic algorithm: NSGA-II,\" IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182-197, 2002.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 5, "raw": "C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, \"Handling multiple objectives with particle swarm optimization,\" IEEE Transactions on evolutionary computation, vol. 8, no. 3, pp. 256-279, 2004.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 6, "raw": "E. Zitzler and S. Künzli, \"Indicator-based selection in multiobjective search,\" in International Conference on Parallel Problem Solving from Nature, 2004, pp. 832-842: Springer.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 7, "raw": "Q. F. Zhang and H. Li, \"MOEA/D: A multiobjective evolutionary algorithm based on decomposition,\" (in English), Ieee Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, Dec 2007.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 8, "raw": "J. Bai and H. Liu, \"Multi-objective artificial bee algorithm based on decomposition by PBI method,\" Applied Intelligence, vol. 45, no. 4, pp. 976-991, 2016.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 9, "raw": "E. ERGUL, \"Çok amaçlı genetik algoritmalar: Temelleri ve Uygulamaları,\" ph.D., Multi Objective Optimization, Ondokuz Mayıs University, SAMSUN, 2010.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 10, "raw": "A. J. Nebro and J. J. Durillo, \"A study of the parallelization of the multi-objective metaheuristic MOEA/D,\" in International Conference on Learning and Intelligent Optimization, 2010, pp. 303-317: Springer.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 11, "raw": "J. J. Durillo and A. J. Nebro, \"jMetal: A Java framework for multi-objective optimization,\" Advances in Engineering Software, vol. 42, no. 10, pp. 760-771, 2011.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 12, "raw": "E. Zitzler, K. Deb, and L. Thiele, \"Comparison of multiobjective evolutionary algorithms: Empirical results,\" Evolutionary computation, vol. 8, no. 2, pp. 173-195, 2000.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 13, "raw": "K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, \"Scalable test problems for evolutionary multiobjective optimization,\" in Evolutionary multiobjective optimization: Springer, 2005, pp. 105-145.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 14, "raw": "Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, \"Multiobjective optimization test instances for the CEC 2009 special session and competition,\" University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, vol. 264, 2008.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 15, "raw": "A. Babalik, A. Ozkis, S. A. Uymaz, and M. S. Kiran, \"A multi-objective artificial algae algorithm,\" (in English), Applied Soft Computing, vol. 68, pp. 377-395, Jul 2018.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 16, "raw": "Y. Xiang, Y. Zhou, and H. Liu, \"An elitism based multi-objective artificial bee colony algorithm,\" European Journal of Operational Research, vol. 245, no. 1, pp. 168-193, 2015.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null }, { "order": 17, "raw": "D. H. Wolpert and W. G. Macready, \"No free lunch theorems for optimization,\" IEEE transactions on evolutionary computation, vol. 1, no. 1, pp. 67-82, 1997.", "doi": null, "title": null, "authors": null, "source": null, "year": null, "volume": null, "pages": null } ], "statements": { "acknowledgements": null, "conflict_of_interest": null, "data_availability": null, "ai_disclosure": null }, "credit": [], "history": { "published": "2019-12-22" } }