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Öğe The estimation of the electricity energy demand using particle swarm optimization algorithm: A case study of Turkey(ELSEVIER SCIENCE BV, 2017) Gulcu, Saban; Kodaz, HalifeEnergy is the most important factor in improving the quality of life and advancing the economic and social progress. Demographic changes directly affect the energy demand. At present the worlds population is growing quickly. As of 2015, it was estimated at 7.3 billion. The population and the export of Turkey have been increasing for two decades. Consequently, electricity energy demand of Turkey has been increasing rapidly. This study aims to predict the future electricity energy demand of Turkey. In this paper, the prediction of the electricity demand of Turkey is modeled by using particle swarm optimization algorithm. The data of the gross domestic product, population, import and export are used as input data of the proposed model in the experiments. The GDP, import and export data are taken from the annual reports of the Turkish Ministry of Finance. The population data are taken from the Turkish Statistical Institute. The electricity demand data are taken from the Turkish Electricity Transmission Company. The statistical method R-2 and adjusted-R-2 are used as the performance criteria. The experimental results show that the generated model is very efficient. (c) 2017 The Authors. Published by Elsevier B.V.Öğe A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization(PERGAMON-ELSEVIER SCIENCE LTD, 2015) Gulcu, Saban; Kodaz, HalifeThis article presented a parallel metaheuristic algorithm based on the Particle Swarm Optimization (PSO) to solve global optimization problems. In recent years, many metaheuristic algorithms have been developed. The PSO is one of them is very effective to solve these problems. But PSO has some shortcomings such as premature convergence and getting stuck in local minima. To overcome these shortcomings, many variants of PSO have been proposed. The comprehensive learning particle swarm optimizer (CLPSO) is one of them. We proposed a better variation of CLPSO, called the parallel comprehensive learning particle swarm optimizer (PCLPSO) which has multiple swarms based on the master-slave paradigm and works cooperatively and concurrently. The PCLPSO algorithm was compared with nine PSO variants in the experiments. It showed a great performance over the other PSO variants in solving benchmark functions including their large scale versions. Besides, it solved extremely fast the large scale problems. (C) 2015 Elsevier Ltd. All rights reserved.Öğe A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem(SPRINGER, 2018) Gulcu, Saban; Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, HalifeThis article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master-slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.