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Öğe Artificial algae algorithm (AAA) for nonlinear global optimization(ELSEVIER SCIENCE BV, 2015) Uymaz, Sait Ali; Tezel, Gulay; Yel, EsraIn this study, a novel bio-inspired metaheuristic optimization algorithm called artificial algae algorithm (AAA) inspired by the living behaviors of microalgae, photosynthetic species, is introduced. The algorithm is based on evolutionary process, adaptation process and the movement of microalgae. The performance of the algorithm has been verified on various benchmark functions and a real-world design optimization problem. The CEC'05 function set was employed as benchmark functions and the test results were compared with the algorithms of Artificial Bee Colony (ABC), Bee Algorithm (BA), Differential Evolution (DE), Ant Colony Optimization for continuous domain (ACO(R)) and Harmony Search (HSpop). The pressure vessel design optimization problem, which is one of the widely used optimization problems, was used as a sample real-world design optimization problem to test the algorithm. In order to compare the results on the mentioned problem, the methods including ABC and Standard PSO (SPS02011) were used. Mean, best, standard deviation values and convergence curves were employed for the analyses of performance. Furthermore, mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), which are computed as a result of using the errors of algorithms on functions, were used for the general performance comparison. AAA produced successful and balanced results over different dimensions of the benchmark functions. It is a consistent algorithm having balanced search qualifications. Because of the contribution of adaptation and evolutionary process, semi-random selection employed while choosing the source of light in order to avoid local minima, and balancing of helical movement methods each other. Moreover, in tested real-world application AAA produced consistent results and it is a stable algorithm. (C) 2015 Elsevier B.V. All rights reserved.Öğe Artificial algae algorithm with multi-light source for numerical optimization and applications(ELSEVIER SCI LTD, 2015) Uymaz, Sait Ali; Tezel, Gulay; Yel, EsraArtificial algae algorithm (AAA), which is one of the recently developed bio-inspired optimization algorithms, has been introduced by inspiration from living behaviors of microalgae. In AAA, the modification of the algal colonies, i.e. exploration and exploitation is provided with a helical movement. In this study, AAA was modified by implementing multi-light source movement and artificial algae algorithm with multi-light source (AAA(ML)) version was established. In this new version, we propose the selection of a different light source for each dimension that is modified with the helical movement for stronger balance between exploration and exploitation. These light sources have been selected by tournament method and each light source are different from each other. This gives different solutions in the search space. The best of these three light sources provides orientation to the better region of search space. Furthermore, the diversity in the source space is obtained with the worst light source. In addition, the other light source improves the balance. To indicate the performance of AAA with new proposed operators (AAA(ML)) experiments were performed on two different sets. Firstly, the performance of AAA and AAA(ML) was evaluated on the IEEE-CEC'13 benchmark set. The second set was real-world optimization problems used in the IEEE-CEC'11. To verify the effectiveness and efficiency of the proposed algorithm, the results were compared with other state-of-the-art hybrid and modified algorithms. Experimental results showed that the multi-light source movement (MLS) increases the success of the AAA. (C) 2015 Elsevier Ireland Ltd. All rights reserved.Öğe A Modified Artificial Algae Algorithm for Large Scale Global Optimization Problems(2018) Koçer, Havva Gül; Uymaz, Sait AliOptimization technology is used to accelerate decision-making processes and to increase the quality of decision making inmanagement and engineering problems. The development technology has made real world problems large and complex. Many optimizationmethods that proposed for solving large-scale global optimization (LSGO) problems suffer from the “curse of dimensionality”, whichimplies that their performance deteriorates quickly as the dimensionality of the search space increases. Therefore, more efficient and robustalgorithms are needed. When literature on large-scale optimization problems is examined, it is seen that algorithms with effective globalsearch ability have better results. For the purpose, in this paper Modified Artificial Algae Algorithm (MAAA) is proposed by modifyingoriginal version of Artificial Algae Algorithm (AAA) inspiring by Differential Evolution Algorithm (DE)’s mutation strategies. AAA andMAAA are compared with each other by operating with the first 10 benchmark functions of CEC2010 Special Session on Large ScaleGlobal Optimization. The results show that hybridization process that applied by updating an additional fourth dimension with mutationstrategies of DE after the helical motion of the AAA algorithm, contributes exploration phase and improves the AAA performance onLSGO.Öğe A multi-objective artificial algae algorithm(ELSEVIER, 2018) Babalik, Ahmet; Ozkis, Ahmet; Uymaz, Sait Ali; Kiran, Mustafa ServetIn this study, the authors focus on modification of the artificial algae algorithm (AAA), for multi-objective optimization. Basically, AAA is a population-based optimization algorithm inspired by the behavior of microalgae cells. In this work, a modified AAA with appropriate strategies is proposed for multi-objective Artificial Algae Algorithm (MOAAA) from the first AAA that was initially presented to solve single-objective continuous optimization problems. To the best of our knowledge, the MOAAA is the first modification of the AAA for solving multi-objective problems. Performance of the proposed algorithm is examined on a benchmark set consisting of 36 different multi-objective optimization problems and compared with four different swarm intelligence or evolutionary algorithms that are well-known in literature. The MOAAA is highly successful in solving multi-objective problems, and it has been demonstrated that the MOAAA is an alternative competitive algorithm in multi-objective optimization according to experimental results and comparisons presented in this research topic. (C) 2018 Elsevier B.V. All rights reserved.Öğe UML ile sanal kampüs modellemesi(Selçuk Üniversitesi Mühendislik Fakültesi, 2016) Uymaz, Sait Ali; Kahramanlı, ŞirzatBu çalışmada animasyonlar, görüntülü konferanslar, mobil teknolojiler ve artırılmış gerçeklik ile desteklenen öğrenci odaklı web tabanlı bir uzaktan eğitim sistemi için birleşik modelleme dili (Unified Modeling Language – UML) ile sanal kampüs modellenmesi gerçekleştirilmiştir. Sanal Kampüs modeli 4+1 bakışı içermektedir ve yazılım sürecinin aşamaları farklı UML diyagramları ile modellenmiştir. Bu modelde ilk olarak sistem aktörleri belirlenmiş ve durum diyagramları ile aktörler ve rolleri gösterilmiştir. Daha sonra durum senaryoları ve sınıf diyagramları gösterilmiştir. Ayrıca sistemi etkileyen olaylar ve etkinlikler etkinlik diyagramı ile gösterilmiştir. Son olarak sistemin alt modülleri ve fiziksel cihazları bileşen ve yaygınlaştırma diyagramları ile gösterilmiştir. Analiz ve tasarım aşamasında iyi bir modelleme yazılım sürecinde doğabilecek problemleri önler. Teknolojik yenilikler ile desteklenmiş web tabanlı bir uzaktan eğitim sistemi maliyet avantajları ile birleştiğinde ideal bir eğitim modeli olmaktadır.Öğe Yeni bir biyolojik ilhamlı metasezgisel optimizasyon metodu: Yapay alg algoritması(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2015-12-14) Uymaz, Sait Ali; Tezel, Gülay; Yel, EsraBu çalışmada hareketli mikro alglerin karakteristik özellikleri ve yaşam davranışlarından esinlenilerek yapay alg algoritması (AAA) adında yeni bir biyolojik ilhamlı metasezgisel optimizasyon algoritması sunulmuştur. AAA evrimsel süreç, adaptasyon süreci ve helisel hareket süreci adında 3 temel bölümden oluşmaktadır. Helisel hareket süreci, alglerin sıvı içindeki helisel hareketleri ve ışığa yakın olma davranışları, evrimsel süreç, alglerin mitoz bölünme ile üremeleri ve adaptasyon süreci ise alglerin bulundukları çevreye adapte olmaları gibi temel özellikleri üzerine modellenmiştir. AAA'nın etkinliğini doğrulamak için CEC'05 test fonksiyonlarında ve basınçlı tank tasarım optimizasyon problemi üzerinde performans testleri yapılmıştır. Test sonuçları literatürde bulunan metasezgisel algoritmalar ile kıyaslanmış ve performans analizi yapılmıştır. AAA'ya çoklu ışık kaynağı hareketi (MLS) uygulanarak geliştirilmiş yapay alg algoritması (AAAML) sunulmuştur. AAA ve AAAML yöntemlerinin etkinlikleri ve verimliliklerini doğrulamak için CEC'13 test fonksiyonları ve CEC'11 gerçek dünya optimizasyon problemleri üzerinde testler yapılmıştır. Test sonuçları literatürde iyi bilinen algoritmaların geliştirilmiş ve hibrit sürümleri ile kıyaslanmış ve detaylı performans analizleri yapılmıştır. Yapılan detaylı performans testleri ve analizler AAA ve geliştirilmiş sürümü AAAML'nin sürekli optimizasyon için tutarlı, etkin ve verimli yöntemler olduğunu göstermiştir.
