Person: KUYU, YİĞİT ÇAĞATAY
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KUYU
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YİĞİT ÇAĞATAY
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Publication Gozde: A novel metaheuristic algorithm for global optimization(Elsevier, 2022-11-01) Kuyu, Yiğit Çağatay; Vatansever, Fahri; KUYU, YİĞİT ÇAĞATAY; VATANSEVER, FAHRİ; Elektrik Elektronik Mühendisliği Bölümü; 0000-0002-3885-8622; AAG-8425-2021; AAC-6923-2021This study proposes a new metaheuristic algorithm, called "Geometric Octal Zones Distance Estimation "(GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with the information sharing between the zones considering the distance of the zones utilizing median values. The whole population represents the eight zones that are the combination of different search strategies to guide knowledge dissemination from one zone to others in the search space. To demonstrate the effectiveness of the proposed optimizer, it is compared with two classes of metaheuristics, which are (1) GA, PSO, DE, CS and HS as the classical metaheuristics and (2) BWO, SSA, MVO, HHO, ChOA, AOA and EBOwithCMAR as the up-to-date metaheuristics. The search capability of the proposed algorithm is tested on two different numerical benchmark sets including low and high dimensional problems. The developed algorithm is also adapted to ten real world applications to handle constraint optimization problems. In addition, to further analyse the results of the proposed algorithm, three well-known statistical metrics, Friedman, Wilcoxon rank sum and Whisker-Box statistical tests are conducted. The experimental results statistically show that GOZDE is significantly better than, or at least comparable to the twelve metaheuristic algorithms with outstanding performance in solving numerical functions and real-world optimization problems. (C) 2022 Elsevier B.V. All rights reserved.Publication Comparison of the evolutionary algorithm's performances on power flow analysis(Pamukkale Univ, 2018-01-01) Kuyu, Yiğit Çağatay; KUYU, YİĞİT ÇAĞATAY; Erdem, Nergis; ERDEM, NERGİS; Vatansever, Fahri; VATANSEVER, FAHRİ; Yılmaz, Güneş; YILMAZ, GÜNEŞ; Mühendislik Fakültesi; Elektrik ve Elektronik Mühendisliği Bölümü; 0000-0002-3885-8622; 0000-0001-8972-1952; AAG-8425-2021; AAC-6923-2021; AAH-4182-2021; AAH-4017-2021Power flow in energy systems is one of the major problems. Several classical analysis methods are utilized for solving this problem. However, power generation limits, valve loading effects of units also makes the power flow problem become much harder to solve in the system. In this case, it is possible to achieve the most appropriate solutions with evolutionary algorithms. In this study, optimal power flow problems are solved under same beginning conditions, comprehensively performed with evolutionary algorithms which are recently used and associated algorithm performance is analyzed in IEEE 30-bus test system for two cases. Energy gains of algorithms are obtained; the best, worst and mean values found from optimization are evaluated; convergence analyses are performed comparatively. Thus the effectiveness and efficiency of evolutionary algorithms are clearly demonstrated on solution of optimal power flow problems.Publication A hybrid optimizer based on backtracking search and differential evolution for continuous optimization(Taylor & Francis, 2021-01-02) Kuyu, Yiğit Cağatay; Onieva, Enrique; Lopez-Garcia, Pedro; KUYU, YİĞİT ÇAĞATAY; Mühendislik Fakültesi; Elektrik Elektronik Mühendisliği Bölümü; 0000-0002-7054-3102 ; AAC-6923-2021This paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.Publication Modified forensic-based investigation algorithm for global optimization(Springer, 2021-02-26) Kuyu, Yiğit Çağatay; Vatansever, Fahri; KUYU, YİĞİT ÇAĞATAY; VATANSEVER, FAHRİ; Mühendislik Fakültesi; Elektrik Elektronik Mühendisliği Bölümü; 0000-0002-3885-8622; 0000-0002-7054-3102; AAG-8425-2021; AAC-6923-2021Forensic-based investigation (FBI) is recently developed metaheuristic algorithm inspired by the suspect investigation-location-pursuit operations of police officers. This study focuses on the search processes of the FBI algorithm, called Step A and Step B, to improve and increase its performance. For this purpose, opposition-based learning is adopted to Step A to enhance diversity, while Cauchy-based mutation is integrated with Step B to guide the search to different regions and to jump out of local minima. To show the effectiveness of these improvements, the proposed algorithm has been tested with two different benchmark sets. To verify the performance of the new modified algorithm, the statistical test is carried out on numerical functions. This study also investigates the application of the proposed algorithm to a set of six real-world problems. The proposed and adapted/integrated methods appear to have a significant impact on the FBI algorithm, which augments its performance, resulting in better solutions than the compared algorithms in most of the functions and real-world problems.Publication Advanced metaheuristic algorithms on solving multimodal functions: Experimental analyses and performance evaluations(Springer, 2021-12) Kuyu, Yiğit Çağatay; Vatansever, Fahri; KUYU, YİĞİT ÇAĞATAY; VATANSEVER, FAHRİ; Elektrik Mühendisliği Bölümü; 0000-0002-3885-8622; 0000-0002-7054-3102; AAC-6923-2021; AAG-8425-2021Optimization problems encountered in real-world have multiple local minimums. Multimodal functions can well represent many real-world applications as they include two or more local minimum points in nature. Numerous metaheuristic algorithms aim to find the best balance between exploration and exploitation, and better algorithms have been developed during the search for such a balance. Therefore, it becomes necessary to answer the question: Which metaheuristic algorithm is the best-suited algorithm among the metaheuristics that have been developed? This study presents a comprehensive and fair investigation of the seven metaheuristic algorithms developed in the last five years on twenty multimodal functions with a wide range of dimensions commonly used in literature. Each is subject to the same initial conditions but with three different performance criteria. The strengths and weaknesses of the each algorithm were demonstrated for each criterion and the experimental results were analyzed statistically by using the Friedman test. Furthermore, to the best of our knowledge, this is the first attempt to address these challenging problems, in combination with these algorithms and performance metrics, which can also give a further insight to the researchers for choosing appropriate algorithms in the context of global optimization.Publication A comparative study of the state-of-the-art algorithms on multi-objective problems using performance metrics(Ieee, 2019-01-01) Kuyu, Yigit Çağatay; KUYU, YİĞİT ÇAĞATAY; Vatansever, Fahri; VATANSEVER, FAHRİ; Mühendislik Fakültesi; Elektrik ve Elektronik Mühendisliği Bölümü; 0000-0002-3885-8622; AAC-6923-2021; AAG-8425-2021Most real-world optimization problems have several objectives which can require to satisfy simultaneously. As the complexity of the problems increases, the problems become more challenging to solve for the algorithms. Over the last few years, many state-of-the-art multi-objective optimization algorithms (MOOAs) have been developed but, to the best knowledge of the authors, there has been no comprehensive comparative study in the literature together with these algorithms. Moreover, there are many parameters that affect the performances of algorithms, such as the population size, iteration number, and initial population. These values may also differ from one study to another, making comparisons more difficult to perform fairly. In this study, four MOOAs which were not previously used together for the comparisons of performance are applied to find optimal solutions of various mathematical optimization problems with the same initial conditions. The performances of the algorithms are evaluated by using two different performance metrics. In addition, Pareto fronts found by the algorithms are given comparatively. Experimental results give an overview for the performance of each algorithm on the different type of the problems by making fair comparison.