H
Hamdi Tolga Kahraman
Researcher at Karadeniz Technical University
Publications - 38
Citations - 406
Hamdi Tolga Kahraman is an academic researcher from Karadeniz Technical University. The author has contributed to research in topics: Search algorithm & Optimization problem. The author has an hindex of 8, co-authored 37 publications receiving 144 citations. Previous affiliations of Hamdi Tolga Kahraman include Gazi University.
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Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms
TL;DR: A new selection method based on fitness-distance balance (FDB) is developed in order to solve the premature convergence problem in the MHS process and makes a significant contribution to the meta-heuristic search process.
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A novel stochastic fractal search algorithm with fitness-Distance balance for global numerical optimization
TL;DR: The results of the analysis showed that the problem of premature convergence had been largely eliminated by the application of the FDB method and that the exploitation-exploration balance was also effectively provided, and the proposed FDBSFS algorithm ranked first among the thirty-nine competing algorithms.
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Fitness–Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources
TL;DR: In this article, the adaptive guided differential evolution (AGDE) algorithm was improved by using the Fitness-Distance Balance (FDB) method with its balanced searching and high-powered diversity abilities.
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Development of the Multi-Objective Adaptive Guided Differential Evolution and optimization of the MO-ACOPF for wind/PV/tidal energy sources
TL;DR: This study presents the Multi-Objective Adaptive Guided Differential Evolution (MOAGDE) as a powerful and stable algorithm that can effectively find Pareto optimal solutions for multi-objective optimization problems with different types of high-complexity decision/objective spaces.
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Development of a Lévy flight and FDB-based coyote optimization algorithm for global optimization and real-world ACOPF problems
TL;DR: An improved version of the coyote optimization algorithm (COA) that is more compatible with nature and exhibited a definite superiority over both the constrained and highly complex real-world engineering ACOPF problem and the unconstrained convex/nonconvex benchmark problems is presented.