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Karar vermenin zorluğu ve matematiğin gelişimi, karmaşık sistemlerde en iyi çözüme ulaşmak için matematiksel uygulamaların ortaya çıkmasına neden olmuştur. Matematiksel uygulamalardan biri olan optimizasyon yönetim, ekonomi, planlama, mühendislik gibi birçok alanda en iyi çözümü sunan yöntemleri içermektedir. Parçacık Sürü Optimizasyonu (PSO) tekniği popülasyon temelli sezgisel bir optimizasyon tekniğidir. Kuş veya balık sürülerinin sosyal davranışlarının benzetimi ile geliştirilmiştir. Parçacık Sürü Optimizasyonu tekniği çok parametreli ve çok değişkenli optimizasyon problemlerine çözüm bulmak için kullanılmaktadır. Bu çalışmada da Parçacık Sürü Optimizasyon tekniğinin teorik yapısı anlatılarak emtia piyasası ürünleri ile portföy optimizasyonu yapılmıştır.
The difficulty of making decisions and the development of mathematics, mathematical applications to achieve the best solution in complex systems has led to the emergence. Which is one of the applications of mathematical optimization, management, economics, planning, engineering includes many areas such as methods of providing the best solution. Today's businesses to use resources efficiently, fund surpluses must strengthen systems to assess the decision-making in order to achieve such objectives.With globalization in a rapidly changing business and economic way to adapt to its environment, decision-makers to broaden the decision-making systems passes. A mathematical model, describing a process or event, in mathematical terms, a series of formulas. Mathematical equations intended to describe the real world. There are many reasons modellemeni use in solving a problem. As an example of determining the number of fish in a river or the sea, the cost to apply experimental methods to find the solution of the problem may be too high. How to move objects in an environment of zero gravity, such as that they were not putting, would be impossible to apply the experimental paths. In the real world of experimental difficulties in handling mathematical modeling to predict how to act in any case where the systems referenced. The difficulty of decision making in the emerging markets after the Second World War, this area has led to the development of some techniques for. This technique is called optimization techniques in general. Optimization techniques can be described as optimization studies. These studies offer us the best of many variables, in terms of time and cost savings can be achieved. Choices made among alternatives, not experience, modified according to mathematical models provide a more healthy results. Numerical methods of optimization techniques, linear programming is described as the first technique developed in this regard. However, in reality the structure exhibits a non-linear many problems have led to the development of many other techniques more. Mathematical models are developed in harmony with real life problems arise over time, have led researchers to examine patterns of life in nature. Population-based techniques have emerged as a result of this orientation. These techniques, birds, ants, bees, and so on.colonies and analyzed by these techniques have various social simulation performed in many different areas. Particle Swarm Optimization (PSO) technique is a population-based heuristic optimization technique. Simulation of social behavior of flocks of birds or fish has been developed. Particle Swarm Optimization and multivariate multi-parameter optimization technique is used to find solutions to problems. Particle Swarm Optimization (PSO) technique as flocks of birds or fish have been developed inspired by social behavior. Developed by examining the behavior of individuals interacting with each other and the environment. This concept is also called particle intelligence. PSO named as particle potential solutions, the best solutions to the current problem space by following the surfing. PSO, function optimization, scheduling, training neural networks, fuzzy logic systems, image processing, portfolio optimization and so on. is widely used in many fields. A specific area of the core logic of Particle Swarm Optimization, is just one region, food, food in this area looking for a bird group, and initially did not know where the food is accepted, the food and attempted to answer the question of what could be the best solution to find. Mind, that gets to the genetic path of love, fear, jealousy, as well as a natural defense motives şartlandırmalarından current environment and the community received the influence of interactions evolving.So the mind is not fixed, but can not increase until the end of human life and is a skill that can develop. Mind, the machine, the computer software, or otherwise can not be imitated Particle Swarm Optimization and multivariate multi-parameter optimization technique is used to find solutions to problems. PSO, such as genetic algorithms shows many similarities with evolutionary computation techniques. The system starts a population with random solutions and searches for optimum solutions by updating generations. The notion of a social system is a simplified Particle Swarm square is simulated. The main goal is to make the simulation graphically, but the elegant choreography of a flock of birds is unpredictable.Comparison of simulations combined with first rate close to the neighborhood, the elimination of covariates and distance, accelerate research and changed to a unified multi-dimensional. At a certain point in the evolution of algorithms, a conceptual model was the difference, really, this one from the optimizer. From the beginning to the end of a trial and error, a few parameters for the optimization algorithmto eliminate the leaders of the secondary. Herd brought together representatives from the term structure to refer to each other, affecting the senses are used in general. Called because a flock of bees, but lots of lots of classic example of expansion of the other systems of similar nature. A lot of an ant colony where ants may be considered as individual representatives, where a bevy of birds of representatives, a sürüdür cars in traffic, a crowded flock of humanity, a flock of immune system cells and molecules, and the economy, representatives of the flock of the economy. A flock of birds, although it is the concept of herd-like, space is recommended in the opinion of joint action, and this relates to all common types of behavior, not related to the spatial movement Some of the genetic mechanisms that underlie such cooperation can be defined as: for example, multi-shaped ant species, such as the difference between the larger and more junior division of labor can conduct the anatomical differences between individuals.However, many views on the joint activities of social insects is that the selforganizing. In fact, in the context of physics and chemistry of macroscopic (large scale) models, processes and interactions outside the self-organizing theory developed to describe the emergence of a macroscopic level, the social insects, the complex interactions between individuals can undermine the common behavior shows a simplebehaving. In such cases there is no need to resort to complex social behavior to explain the complexity of individual. In 1952, Harry Markowitz's Modern Portfolio management is considered as the beginning of the article published by Porfolio Selection. Only by diversifying its portfolio, explains the importance of relationships between securities.Markowitz meanvariance model, developed in 1956, restructured the Modern Portfolio theory. The Markowitz theory by evaluating the past performances of the security's expected return and aims to predict. The general framework of the Modern Portfolio Theory founder, Harry Markowitz's approach today, an investor-owned securities and to deposit a certain amount, the money is to keep a term. This approach is based on a portfolio of securities selected by the investor potential portfolios. Commodities, gold, silver, copper, crude oil, natural gas, coal, wheat, cotton, corn, sugar, coffee products, such as formed by nature to be the subject goods, the name given to trade.Commodities are used to form a more valuable and different products, and the values determined by the consumer, valuable, chemical products, emission products, animal products and energy products. All of these products "commodity" is called. Commodity products are natural disasters, crises, that may fluctuate due to changes in seasonal products. These products generated by the portfolio optimization techniques were used in order to manage effectively and efficiently. Commodity products, economic, social and political changes affected. These changes are sources of systematic risk. Capital market returns are affected by commodity price movements in the general market risk, interest rate risk through its effect on commodity prices, changes in the level of inflation due to commodity inflation risk returns, systematic risk variety, which lead to change. Particle Swarm Optimization technique in this study by explaining the theoretical structure of the commodity market with products made of portfolio optimization. Particle Swarm Optimization technique used in this study, what conclusions can be set in advance for portfolio optimization and interpretation of these results will be provided. Particle Swarm Optimization technique and portfolio weights with the optimal balance of risk and return will be achieved.
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