Highway accident number estimation in Turkey with Jaya algorithm

In the transportation sector in Turkey, approximately 90% of cargo and passenger transportation is carried out on highways. In recent years, increasing population and welfare levels have brought along an increase in demand for and intensity of highway use. Accidents experienced along with the increased intensity in the use of highways result in fatalities and loss of property. In order to minimize such losses on the highways and determine plans and programs for the future by benefiting from historical data, it is necessary to conduct accurate, consistent, effective, and reliable accident estimations. In the study, highway accident number estimation (HANE) in Turkey was made by using the meta-heuristic Jaya optimization algorithm. For HANE, Jaya linear (Jaya-L) and Jaya Quadratic (Jaya-Q) models were proposed. Indicators such as the number of accidents that occurred between 2002 and 2018, population, gross domestic product (GDP), total divided road length (TDRL), and the number of vehicles were taken for HANE. Indicators were analyzed for four different conditions. HANE was made by using Population–GDP–TDRL–Number of Vehicle indicators together. A total of 75% of the total 17-year data between 2002 and 2018 were used for training purposes, and 25% of the data were used for testing. The results of the proposed Jaya-L and Jaya-Q models were analyzed by comparing them with the Andreassen estimation model (AEM) and multiple linear regression (MLR) methods. Following the successful training and testing results, low, expected, and high scenarios were proposed, and the number of accidents between 2019 and 2030 was estimated. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature

Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey

Estimation of energy demand beforehand is a quite significant problem in respect of economy and sources of country. In this study, Gravitational Search Algorithm (GSA) was modified by making some innovations in GSA and called as Modified Gravitational Search Algorithm (MGSA). Energy demand estimation is conducted through the relationship between the increase in economic indicators in Turkey and energy consumption. Estimation was actualized by using gross domestic product (GSYH), importation, exportation and demography for energy demand estimation and both linear and exponential equations. Energy demand between the years 2017-2037 was predicted by using the data belong to 1997-2011. The years between 2012 and 2016 were used as test data. It was observed that the results acquired via MGSA estimate better compared to GSA results

Tesis Yerleştirme (p-Hub) Probleminin Yapay Arı Kolonisi Kullanılarak Çözülmesi

Tesis (p-Hub) yerleştirme problemi, mal, hizmet ve bilgi dağıtım sistemi stratejilerini konumlandırmayı amaçlayan polinomsal zamanda doğrulanabilen karar problemlerinin karmaşıklık sınıfı olarak bilinmektedir. Dağıtım sistemlerinde istenen düzeyde bir hizmet kalitesini kabul edilebilir bir maliyetle elde etmek için birbirine tahsis edilmiş hatlarla birbirine bağlanmış düğümlerden oluşan bir ağ tasarlanabilir. Tasarlanan bu ağın uygun çözüm maliyetli olmayabilir. Bundan dolayı toplam ulaşım maliyetini azaltmatabilmek amacıyla, diğer düğümler için birleştirme veya yönlendirme noktası olarak çalışan bazı tesisler (hublar) kullanılabilir. Taşımacılık yönetimi, kentsel yönetim, servis merkezlerinin konumlandırılması, sensör ağlarının tasarımı, bilgisayar mühendisliği, bilgisayar ağlarının tasarımı, iletişim ağlarının tasarımı, güç mühendisliği, onarım merkezlerinin konumunu, elektrik hatlarının bakımı ve izlenmesi ile imalat sistemlerinin tasarımı gibi sorunların çözümünde bu tür ağları oluştururken hub'lar kullanılmaktadır. Hub'lı zorlu bir nokta, hangi düğümlerin ağ özelliklerinin farklılık gösterebileceğine ve hub konum noktaları olarak kullanılacağına karar vermektir. Hub’lı yer tahsisinde kısa zamandaki iyi bir çözüm, uzun hesaplamalar sonucunda elde edilen en iyi çözümden daha etkilidir. Hem kısa zamanda hemde optimum çözüm elde edebilmek amacıyla p-Hub problemlerinin çözümünde son zamanlarda sezgisel temelli algoritmalar işe koşulmaktadır. Bundan dolayı bu çalışmada p-Hub konum problemini çözmek için Yapay Arı Koloni (YAK) algoritması önerilmiştir. Bu çalışmada, YAK algoritması p-Hub yer tahsisi problem çözümü için düğüm sayısına bağlı olarak üç farklı durumda uygulanmıştır. Birinci durum merkezde sabit olarak bulunan üç adet tesis ve toplam yirmi düğüm, ikinci durum merkezde sabit altı adet tesis ve bunlara bağlı otuz düğüm, üçüncü durum ise merkezde sabit yedi tesis ve bu tesislere bağlı kırk düğümden oluşmaktadır. YAK algoritması ile elde edilen minimum yer tahsisi maliyet fonksiyonu çözümleri tablolar ve grafiklerle verilmiştir. Elde edilen sonuçlar literatürde yer alan Parçacık Sürü Optimizasyonu sonuçları ile karşılaştırılmıştır. Çalışma sonucunda p-Hub yer tahsisi problem çözümünde YAK’ın daha iyi sonuç elde ettiği görülmüştür. Bundan dolayı yönerilen YAK algoritmasının tesis tahsisi (p-Hub) problemi çözümü için uygun bir yöntem olduğunu göstermiştir

Parçacık Sürü Optimizasyon Algoritması Kullanılarak Optimum Robot Yolu Planlama

Robot yolu planlama problemi robotik ve otomasyon alanı için önemli problemlerden bir tanesidir. Robotların yüksek çalışma hızı, kontrol sistemlerinden aşırı performans gerektirdiği için robot hareketinin doğruluğu ve yol planlaması önem arz etmektedir. Robot yol planlama işleminde, bir başlangıç noktasından son noktaya kadar robotun var olan engellere takılmadan en kısa bir şekilde geometrik bir yol çizerek varış noktasına ulaşması amaçlanır. Robot yol planlama problemi arama yapılan alan uzayında birçok yol seçeneğinin bulunması ve bu yollar arasında en kısa mesafenin karar verilmeye çalışılması nedeniyle zor problemler sınıfına girmektedir. Klasik robot yolu planlama yöntemleri problem karmaşıklaştıkça çözüm bulmakta zorlanmaktadır. Bundan dolayı son yıllarda robotik alanında yol planlama probleminin optimum çözümü için sezgisel yöntemlerin önemi artmaktadır. Robot yolu planlama problemi için literatürde birçok sezgisel algoritma probleminin farklı uygulamaları için kullanılmıştır. Bu çalışmada başlangıç noktasında yer alan bir robotun varış noktasına gidene kadar karşılaşacağı engellere çarpmadan en kısa yolu kullanacak şekilde bitiş noktasına ulaşması için Parçacık Sürü Optimizasyon (PSO) algoritması kullanılarak yol planlama işleminin simülasyonu yapılmıştır. Başlangıç noktası sabit A(0,0) olan ve üç farklı bitiş noktalarına B(4,6), C(6,8) ve D(8,10) göre PSO algoritması ile en kısa robot yolu hesaplanmıştır. Aynı zamanda her bir farklı varış noktası için çalışmada engellerin konumları da değiştirilerek simülasyon işlemi yapılmıştır. Bu şekilde üç farklı konumda robot yolu planlaması çözülmeye çalışılmıştır. Çalışmada kullanılan engeller daire şeklinde olduğundan başlangıç ve bitiş noktaları arasındaki mesafeyi bulmak için bir nokta ve bir doğruya uzaklığının matematiksel formülü kullanılmış ve bu şekilde dairesel engellerden kaçınılmaya çalışılmıştır. PSO algoritması ile yapılan robot yolu planlama problem çözümü her bir durum için tablolar ve grafikler ile gösterilmiştir. PSO ile yapılan çalışma sonuçlarına göre üç farklı durumda robot yolunun en kısa hesaplamaları bulunmuştur. Bu şekilde PSO algoritması çözümlerinin robot yolu planlaması için uygulanabilir olduğu gösterilmiştir

JayaL: A Novel Jaya Algorithm Based on Elite Local Search for Optimization Problems

Many metaheuristic methods have been proposed to solve engineering problems in literature studies. One of these is the Jaya algorithm, a new population-based optimization algorithm that has been suggested in recent years to solve complex and continuous optimization problems. Jaya basically adopts the best solution by avoiding the worst ones. Although this process accelerates the convergence for the solution, it causes concessions in the population and results in inadequate local search capacity. To increase the search capability and exploitation performance of the Jaya algorithm, a new local search procedure—Elite Local Search—has been added to the Jaya algorithm in this study without making any changes in its basic search capability. Thus, an efficient and robust strategy that can overcome continuous optimization problems is presented. This new algorithm created with the elite local search procedure is called JayaL. To demonstrate the performance and accuracy of JayaL, 20 different well-known benchmark functions in the literature were used. In addition to JayaL algorithm, these functions were solved with differential evolution (DE), particle swarm optimization (PSO), artificial bee colony (ABC), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), atom search optimization (ASO) and Jaya algorithms. The performances of JayaL, DE, PSO, ABC DA, GOA, ASO and Jaya algorithms were compared with each other, and experimental results were supported by convergence graphs. At the same time, JayaL has been applied to constrained real-world engineering problems. According to the analyses, it has been concluded that JayaL algorithm is a robust and efficient method for continuous optimization problem

A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

WOS: 000482742800042The economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey's 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems

Solution of economic dispatch problem for wind-thermal power systems by a modified hybrid optimization method

WOS: 000472481600015Economic Dispatch Problem (EDP) is a complex, constrained and non-linear optimization problem. In the EDP, it is aimed to minimize the system fuel cost between minimum and maximum limits of the active power buses. In this study, a modified hybrid Gravitational Search- Teaching-Learning Based Optimization Algorithm (MHGT), a quick, efficient and reliable method is proposed by combining standard Gravitational Search Algorithm (GSA) and Teaching-Learning Based Optimization (TLBO). The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. The MHGT was tested experimentally by well-known and mostly used ten benchmark function in the literature. The proposed method was first implemented on a 6 bus wind-thermal power system for 400, 450 and 500 MW powers. Then, it was implemented on Turkey 19 bus wind-thermal power system according to different ratios of the installed power as 25, 27.5 and 30 percent to solve the EDP problem. The obtained results were compared with the results of other studies. From the results, it is seen that the proposed MHGT method finds the solution in a short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time

A new hybrid gravitational search-teaching-learning-based optimization method for energy demand estimation of Turkey

WOS: 000478687000066In this study, energy demand estimation (EDE) was implemented by a proposed hybrid gravitational search-teaching-learning-based optimization method with developed linear, quadratic and exponential models. Five indicators: population, gross domestic product as the socio-economic indicators and installed power, gross electric generation and net electric consumption as the electrical indicators, were used in analyses between 1980 and 2014. First, the developed models were trained by the data between 1980 and 2010, and then, accuracy of the models was tested by the data between 2011 and 2014. It is found that the obtained results with the proposed method are coherent with the training data with correlation coefficients in three models as 0.9959, 0.9964 and 0.9971, respectively. Root mean square error values were computed 1.8338, 1.7193 and 1.5497, respectively, and mean absolute percentage errors were obtained as 2.1141, 2.0026 and 1.6792%, respectively, in the three models. These values calculated by the proposed method are better than the results of standard gravitational search algorithm and teaching-learning-based optimization methods and also classical regression analysis. Low, expected and high scenarios were proposed in terms of various changing rates between 0.5 and 1.5% difference in socio-economic and electrical indicators. Those scenarios were used in the EDE study of Turkey between 2015 and 2030 for a comparison with other related studies in the literature. By the proposed method, the strategy in energy importation can be regulated and thus more realistic energy policies can be made