The energy demand estimation commands great importance for both developing and developed countries in terms of the economy and country resources. In this study, the differential evolution algorithm (DE) was used to forecast the long-term energy demand in Turkey. In addition to being employed for solving regular optimization problems, DE is also a global, meta-heuristic algorithm that enables fast, reliable and operative stochastic searches based on population. Considering the correlation between the increase in certain economic indicators in Turkey and the increase of energy consumption, two equations were used-one applying the linear form and the other the quadratic form. Turkey's long-term energy demand from 2012 to 2031 was estimated through the DE method in three different scenarios and in terms of the gross domestic product, import, export and population. To prove the success of the DE method in addressing the energy demand problem, the DE method was compared to other methods found in the literature. Results showed that the proposed DE method was more successful than the other methods. Furthermore, the future projections of energy demand obtained using the proposed method were compared to the indicators of energy demand estimated and observed by the Ministry of Energy and Natural Resources.

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Wind turbines -which are significant in terms of clean energy production globally -are environmentally friendly, consistent and economical systems. Wind turbines, due to developing technology, have become one of the most widely used renewable energy resources, and every country has worked to satisfy its electricity demands with the help of wind energy. As the importance of wind energy increases all around the world, the importance of wind turbine placement also rises. In this study, the aim was to position wind turbines over a certain area of a wind farm to obtain maximum turbine power with minimum investment cost, thereby achieving the highest power efficiency. The experimental studies were conducted over a 2×2 km area; this area was divided into a 10×10 grid, and a 20×20 grid for more efficient placement. Because these operations occurred in a binary search space, Invasive Weed Optimization (IWO) -normally used to solve unceasing optimization problems -was used in this study by obtaining fourteen different binary Invasive Weed Optimization (BIWO1 to BIWO14) algorithms with the help of ten different transfer functions (four from the sshaped family, four from the v-shaped family, two based on modulo 2, ceil, ceil-round, ceil-floor and round-floor). The proposed method was compared with other studies carried out in the binary search space found in published literature. As a result, it was seen that the proposed algorithm was an efficient algorithm for solving the problem of wind turbine placement to achieve an optimal placement.

Öz Günümüzde, bilişim teknolojilerinin gelişmesiyle birlikte haberleşme ve bilgi güvenliğinin sağlanması için şifrelemenin önemi giderek artmaktadır. Özellikle internet teknolojisinin gelişmesiyle birlikte veri güvenliğinin sağlanması için birçok şifreleme algoritmaları kullanılmaktadır. Şifreleme algoritmaları simetrik ve asimetrik olmak üzere iki başlık altında incelenmektedir. Bu çalışmada ise simetrik ve asimetrik şifreleme algoritmalarının genel özelliklerine yer vermekle birlikte literatürde önemli bir yere sahip asimetrik şifreleme algoritmalarından biri olan RSA algoritması incelenerek RSA algoritmasının şifreleme yöntemleri üzerindeki etkisi analiz edilmiştir. RSA algoritmasının yapısı, genel özellikleri, avantajı ve dezavantajı hakkında bilgilere yer verilmiştir.

Various heuristic algorithms have been proposed in the literature for solving optimization problems. Tree-seed algorithm (TSA) is inspired from relation between trees and seeds that a population-based evolutionary algorithm. when create process of a seed occur in TSA, the position updating of each dimension of the seed is calculated separately. In scope this study, Some changes have been implemented to original TSA. A new operator was added to the position update equation of original TSA when create a seed from tree. This operator is calculated by dynamically according to the dimension of the problem. As the dimension of the problem increases, the value of the this operator decreases. In addition, we determined an upper and a lower bound for the update process of the seed. The Improved Tree Seed Algorithm (ITSA) proposed in this study and the TSA have been tested on some benchmark functions in the literature. As a result, when the experimental results are taken into consideration, it is understood that the proposed algorithm ITSA is obtained more effective results for benchmark functions than TSA. Moreover, it is observed that ITSA found quite successful results compared with TSA for large-scale benchmark problems.

Optimization is employed in solutions of many problems today. Optimization is described as finding the most suitable alternative among many others under the given constraints. Meta-heuristic algorithms used in solutions of the problems are developed upon the behaviors of living creatures in the nature. One of these is Bat Algorithm (BA), an optimization method based on swarm intelligence. BA is a numerical optimization technique developed in recent times. In this paper, it is aimed at improving Bat Algorithm (IBA) by using Differential Evolution Algorithm population strategy instead of population generation method of BA. IBA was tested on 17 benchmark functions with different characteristics. Suggested method has been seen to exhibit better results compared to the original BA.

Optimization, defined as the process of selecting the best one among all of the possible solutions to a problem within certain constraints, is used for the solution of engineering and numerical functions. The metaheuristics algorithm used for the solution of a wide variety of problems has been developed by influencing the behavior of living things in nature. The Artificial Algae Algorithm (AAA), proposed by Uymaz and one of the biologically inspired optimizations, was used in this study. The AAA is one of the various methods of numerical optimization. In this study, a fine tuning of the control parameters for the AAA was implemented over its different kernel parameters. For this process, the AAA was tested on eight different benchmark functions with different characteristics by making finer adjustments over the parameters. The effect of the parameters was analyzed in terms of both solution quality and convergence speed.