Person: KUYU, YİĞİT ÇAĞATAY
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KUYU
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YİĞİT ÇAĞATAY
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Publication Modified forensic-based investigation algorithm for global optimization(Springer, 2021-02-26) Kuyu, Yiğit Çağatay; Vatansever, Fahri; KUYU, YİĞİT ÇAĞATAY; VATANSEVER, FAHRİ; Bursa Uludağ Üniversitesi/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 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; Bursa Uludağ Üniversitesi/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 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; Bursa Uludağ Üniversitesi/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.