Coordinate exhaustive search initialization for big bang big crunch optimization algorithm

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Özet

Optimization Algorithms, especially evolutionary algorithms, have gained wide acceptance among many disciplines such as electrical, control or industrial engineering. The ability to solve an objective or cost function with more unknown parameters than known equations, which make the problem unsolvable by means of deterministic approaches, is the main benefit of using evolutionary algorithms. In all cases optimization algorithms rely on starting with a completely random initial solution set then evolves this set towards better ones in respect to fitness or objective function iteratively. In this study, we have proven that starting from a unique point after a brief local and deterministic search instead of a pure random set is more beneficial in respect to fitness function evaluation count, or computation time. This approach, although it can be applied to any optimization algorithm, is a natural add-on to Big Bang-Big Crunch (BBBC) optimization method.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2017 10th International Conference on Electrical and Electronics Engineering, ELECO 2017
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar933-937
Sayfa sayısı5
ISBN (Elektronik)9786050107371
Yayın durumuYayınlandı - 2 Tem 2017
Etkinlik10th International Conference on Electrical and Electronics Engineering, ELECO 2017 - Bursa, Turkey
Süre: 29 Kas 20172 Ara 2017

Yayın serisi

Adı2017 10th International Conference on Electrical and Electronics Engineering, ELECO 2017
Hacim2018-January

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???event.eventtypes.event.conference???10th International Conference on Electrical and Electronics Engineering, ELECO 2017
Ülke/BölgeTurkey
ŞehirBursa
Periyot29/11/172/12/17

Bibliyografik not

Publisher Copyright:
© 2017 EMO (Turkish Chamber of Electrical Enginners).

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