A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: Improved bee colony algorithm for multiobjective optimization)

Turkish Journal of Electrical Engineering and Computer Sciences · Ocak 2016

YÖKSİS Kayıtları

A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · 2016 SCI-Expanded

Doç. Dr. TAHİR SAĞ →

A new ABC based multiobjective optimization algorithm with an improvement approach IBMO improved bee colony algorithm for multiobjective optimization

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · 2016 SCI-Expanded

Prof. Dr. MEHMET ÇUNKAŞ →

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2011 ISSN: 1300-0632 SCI-Expanded

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A new ABC-based multiobjective optimization algorithm with an improvement approach (IBMO: improved bee colony algorithm for multiobjective optimization)

2016 ISSN: 13000632 SCI-Expanded

Doç. Dr. TAHİR SAĞ →

10.3906/elk-1402-310

A new ABC based multiobjective optimization algorithm with an improvement approach IBMO improved bee colony algorithm for multiobjective optimization

2016 ISSN: 13000632 SCI-Expanded

Prof. Dr. MEHMET ÇUNKAŞ →

10.3906/elk-1402-310

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2022 ISSN: 1300-0632 SCI-Expanded Q4

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The analysis and optimization of CNN Hyperparameters with fuzzy tree model for image classification

2022 ISSN: 1300-0632 Inspec, Scopus, Ei Compendex, Engineering Source, Web of Science

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10.3906/elk-2107-130

The analysis and optimization of CNN Hyperparameters with fuzzy tree model for image classification

2022 ISSN: 1300-0632 SCI Q4

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10.3906/elk-2107-130

Comparison of ML algorithms to distinguish between human or human-like targets using the HOG features of range-time and range-Doppler images in through-the-wall applications

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Dr. Öğr. Üyesi YUNUS EMRE ACAR →

10.55730/1300-0632.3926

Makale Bilgileri

Dergi Turkish Journal of Electrical Engineering and Computer Sciences

ISSN13000632

Yayın TarihiOcak 2016

Cilt / Sayfa24 · 2349-2373

DOI10.3906/elk-1402-310

Scopus ID2-s2.0-84974716031

Erişim🔓 Açık Erişim

Özet This paper presents a new metaheuristic algorithm based on the artificial bee colony (ABC) algorithm for multiobjective optimization problems. The proposed hybrid algorithm, an improved bee colony algorithm for multiobjective optimization called IBMO, combines the main ideas of the simple ABC with nondominated sorting strategy corresponding to the principal framework of multiobjective optimization such as Pareto-dominance and crowding distance. A fixed-sized external archive to store the nondominated solutions and an improvement procedure to promote the convergence to true Pareto front are used. The presented approach, IBMO, is compared with four representatives of the state-of-the-art algorithms: NSGA2, SPEA2, OMOPSO, and AbYSS. IBMO and the selected algorithms from specialized literature are applied to several multiobjective benchmark functions by considering the number of function evaluations. Then four quality indicators are employed for performance evaluations: general distance, spread, maximum spread, and hypervolume. The results show that the IBMO is superior to the other methods.