Bilgisayar ve Bilişim Fakültesi Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/10834

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Citation - WoS: 67
Citation - Scopus: 79
Djaya: a Discrete Jaya Algorithm for Solving Traveling Salesman Problem
(ELSEVIER, 2021) Gündüz, Mesut; Aslan, Murat
Jaya algorithm is a newly proposed stochastic population-based metaheuristic optimization algorithm to solve constrained and unconstrained continuous optimization problems. The main difference of this algorithm from the similar approaches, it uses best and worst solution in the population in order improve the intensification and diversification of the population, and this provides discovering potential solutions on the search space of the optimization problem. In this study, we propose discrete versions of the Jaya by using two major modifications in the algorithm. First is to generate initial solutions by using random permutations and nearest neighborhood approach to create population. Second is the update rule of the basic Jaya algorithm rearranged to solve discrete optimization problems. Due to characteristics of the discrete optimization problem, eight transformation operators are used for the discrete variants of the proposed algorithm. Based on these modifications, the discrete Jaya algorithm, called DJAYA, has been applied to solve fourteen different symmetric traveling salesman problem, which is one of the famous discrete problems in the discrete optimization. In order to improve the obtained best solution from DJAYA, 2-opt heuristic is also applied to the best solution of DJAYA. Once population size, search tendency and the other parameters of the proposed algorithm have been analyzed, it has been compared with the state-of-art algorithms and their variants, such as Simulated Annealing (SA), Tree-Seed Algorithm (TSA), State Transition Algorithm (STA) Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm (GA) and Black Hole (BH). The experimental results and comparisons show that the proposed DJAYA is highly competitive and robust optimizer for the problem dealt with the study. (C) 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 7
A Jaya-Based Approach To Wind Turbine Placement Problem
(TAYLOR & FRANCIS INC, 2023) Aslan, Murat; Gündüz, Mesut; Kıran, Mustafa Servet
Renewable energy resources are natural, clean, economical, and never-ending energy resources. Wind energy is an important clean, cheap, and easy applicable energy sources. On account of this, generation of the energy from wind technology has been raised day by day because of the competition with fossil-fuel power production methods. By depending on increases the number of turbines located in the wind farm, the average power obtains from each wind turbine appreciable reduces due to the existence of wake effects within the wind farm. Therefore, the optimal placement of turbines in a wind farm provides to get optimum wind energy from the wind farm. When the place where the wind turbines are located is considered as NxN grid, a wind turbine can be established to each cell of this grid. Whether a wind turbine is replaced to each cell of the grid or not can be modeled as a binary-based optimization problem. In this study, a Jaya-based binary optimization algorithm is proposed to determine which cells are used for wind turbine replacement. In order to justify the efficiency of the proposed approach, two different test cases are considered, and the solutions produced by the proposed approach are compared with the solutions of the swarm intelligence or evolutionary computation methods. According to the experiments and comparisons the Jaya-based binary approach shows a superior performance than compared approaches in terms of cost and power effectiveness. While the efficiency of the Jaya-based approach is 92.2% with 30 turbines replacement on 10 x 10 grid, the efficiency of the Jaya-based binary method is 95.7% with 43 turbines replacement on 20 x 20 grid.
Citation - WoS: 89
Citation - Scopus: 97
Jayax: Jaya Algorithm With Xor Operator for Binary Optimization
(ELSEVIER, 2019) Aslan, Murat; Gündüz, Mesut; Kıran, Mustafa Servet
Jaya is a population-based heuristic optimization algorithm proposed for solving constrained and unconstrained optimization problems. The peculiar distinct feature of Jaya from the other population-based algorithms is that it updates the positions of artificial agent in the population by considering the best and worst individuals. This is an important property for the algorithm to balance exploration and exploitation on the solution space. However, the basic Jaya cannot be applied to binary optimization problems because the solution space is discretely structured for this type of optimization problems and the decision variables of the binary optimization problems can be element of set [0,1]. In this study, we first focus on discretization of Jaya by using a logic operator, exclusive or - xor. The proposed idea is simple but effective because the solution update rule of Jaya is replaced with the xor operator, and when the obtained results are compared with the state-of-art algorithms, it is seen that the Jaya-based binary optimization algorithm, JayaX for short, produces better quality results for the binary optimization problems dealt with the study. The benchmark problems in this study are uncapacitated facility location problems and CEC2015 numeric functions, and the performance of the algorithms is compared on these problems. In order to improve the performance of the proposed algorithm, a local search module is also integrated with the JayaX. The obtained results show that the proposed algorithm is better than the compared algorithms in terms of solution quality and robustness. (C) 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 4
Citation - Scopus: 6
Modified Coot Bird Optimization Algorithm for Solving Community Detection Problem in Social Networks
(Springer London Ltd, 2024) Aslan, Murat; Koç, İsmail
Community detection (CD) is a powerful way to extract meaningful information from networks such as political election networks, biological networks, social networks, technological networks. This study proposes a modified discrete version of Coot bird natural life model (COOT) optimization algorithm to solve CD problem in the networks. The basic COOT method is based on the different collective behaviors of the birds of the coot family. These collective actions of coots are regular and irregular movements on the water surface. The position update rule of the basic COOT method does not provide a balance between exploitation and exploration ability for the problem addressed in this study. Therefore, a new update mechanism is integrated into the basic COOT method to extend the local and global search tendencies of the basic COOT method. In the proposed COOT method (for short MCOOT), in order to create a new position for the current coot individual, first the original update mechanism of COOT method is carried out; then, the proposed update mechanism is executed. Three important modifications have been made in the new update mechanism: (1) Some dimensions of the current coot individual are randomly selected in the range of 1 to the dimension size of the problem; (2) the selected dimensions of the coot individual are updated according to the proposed update rule; (3) a genetic mutation operator is executed on the current coot position according to a mutation probability to improve the exploration ability. Furthermore, in the proposed MCOOT method, the continuous values of the current coot positions are converted to discrete values, because the CD problem is a discrete problem. Based on these modifications, in order to analyze and validate the effectiveness of the proposed MCOOT, it is applied on ten different small-sized or large-sized network problems. Finally, the experimental results of MCOOT method are compared with those of some state-of-the-art optimization methods in terms of solution quality and time evaluation. According to the experiments of our study, the proposed algorithm is obtained the best results for all community detection problems used in this study when compared with 22 other algorithms. As a result, the proposed method achieves superior or comparable performance in terms of solution quality and robustness according to the general results. Therefore, the proposed method can be much more competitive, especially for discrete problems.

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