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SETSCI - Volume 1 (2017)
ISMSIT2017 - International Symposium on Multidisciplinary Studies and Innovative Technologies, Tokat, Turkey, Dec 02, 2017

Evrimsel Algoritma ile Parçacık Sürü Algoritmasının Simülasyon Tabanlı Karşılaştırılması ve Elde Edilen Sonuçların Analizi
Serkan Dereli1, Raşit Köker2, İsmail Öylek3*, Metin Varan4
1Sakarya University, Sakarya, Turkey
2Sakarya University, Sakarya, Turkey
3Sakarya University, Sakarya, Turkey
4Sakarya University, Sakarya, Turkey
* Corresponding author: ioylek@sakarya.edu.tr
Published Date: 2017-12-08   |   Page (s): 252-255   |    597     6
ABSTRACT Evrimsel algoritma ve parçacık sürü algoritması çok uzun zamandır ayrı ayrı çeşitli karmaşık problemlerin çözümünde yaygın olarak kullanılan sezgisel algoritmalardır. Evrimsel algoritma canlıların çoğalmasını ve hayatta kalmak için en iyilerin seçilmesi tekniğine dayanırken, parçacık sürü algoritması kuş ve balık sürülerinin yiyecek arama davranışlarından esinlenilerek geliştirilmiştir. Her iki algoritmanın da üstün olduğu taraflar vardır. Parçacık sürü algoritması iyi sonuçlara daha iyi yaklaşırken, evrimsel algoritma ise iyilerin yeni nesillere aktarılması ve adaptasyon neticesinde daha kısa çalışma zamanına sahiptir. İşte bu çalışmada konu edilen algoritmaların sahip olduğu bu üstünlüklerin simülasyon tabanlı bir uygulama ile açıkça gösterilmektedir.  

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Evolutionary algorithm and particle swarm algorithm are heuristic algorithms that have been widely used to solve various complex problems for a very long time. While the evolutionary algorithm is based on the idea of increasing living things and choosing the best ones for survival, the particle swarm algorithm has been developed by inspiring food search behaviors of birds and fish. There are sides where both algorithms are superior. While the particle swarm algorithm better approximates the results, the evolutionary algorithm has a shorter working time due to the transfer of goodness to the next generation and adaptation. In this work, it is a study in which the advantages of the algorithms are clearly demonstrated by a simulation-based application.
KEYWORDS evolutionary algorithm, particle swarm algorithm, optimization, heuristic method
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