Yazar "Turgut, Oguz Emrah" seçeneğine göre listele

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    Chaotic Aquila Optimization Algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models
    (Springer Singapore Pte Ltd, 2024) Turgut, Oguz Emrah; Turgut, Mert Sinan; Kirtepe, Erhan
    This research study aims to enhance the optimization performance of a newly emerged Aquila Optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random numbers. This work employs 25 different chaotic maps under the framework of Aquila Optimizer. It considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems, which have yet to be studied in past literature works. It was found that Ikeda chaotic map enhanced Aquila Optimization algorithm yields the best predictions and becomes the leading method in most of the cases. To test the effectivity of this chaotic variant on real-world optimization problems, it is employed on two constrained engineering design problems, and its effectiveness has been verified. Finally, phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method, and respective solutions have been compared with those obtained from state-of-art optimizers. It is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems, showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.
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    Chaotic gradient based optimizer for solving multidimensional unconstrained and constrained optimization problems
    (Springer Heidelberg, 2023) Turgut, Oguz Emrah; Turgut, Mert Sinan
    Gradient-based optimizer (GRAD) belongs to the recently developed population-based metaheuristic algorithms inspired by the development of Newton-type methods. Despite its new emergence, there are many successful applications of this optimizer in the existing literature; however, chaos integrated version of this algorithm has not been extensively studied yet. In his study, twenty-one different chaotic maps have been incorporated into the standard GRAD algorithm to maintain a reliable balance between exploration and exploitation mechanisms, which is not robustly constructed within the original algorithm. First ninety-nine thirty dimensional artificially generated optimization benchmark problems comprised of sixty-eight multimodal and thirty-one unimodal functions have been solved by these chaotic variants of the GRAD algorithm to determine the five best performing methods between them. Clear dominancy of the chaotic algorithms is clearly observed over the entire range of benchmark cases in terms of solution accuracy and robustness. Then, to validate the optimization capability of the chaos integrated GRAD algorithms, the best method among them is tested on fourteen constrained real world engineering problems, and its respective feasible results are benchmarked against those obtained from cutting edge metaheuristic optimizer. It is seen that the chaotic GRAD algorithm is able to effectively compete with other state-of-art algorithms on both solving unconstrained and constrained engineering problems. Moreover, it is observed that the Chebyshev chaotic map improved GRAD algorithm outperforms its contemporaries in both unconstrained and constrained cases.
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    Diversity enhanced Equilibrium Optimization algorithm for solving unconstrained and constrained optimization problems
    (Springer Heidelberg, 2023) Turgut, Oguz Emrah; Turgut, Mert Sinan
    This research study proposes a novel mutation scheme mainly based on the manipulation equations of Tangent Search Optimization and mutualism phase of Symbiotic Organism Search algorithms to be implemented on the Equilibrium Optimization algorithm to enhance the solution diversity among the population individuals. Beneficial coordination between these governing search mechanisms enables maintaining diversity in the population. It eliminates the stagnation towards the local sub-optimal solutions over the search domain, significantly alleviating the inherent drawbacks of Equilibrium Optimizer. To assess the efficiency of the proposed diversity-enhanced Equilibrium Optimization algorithm (DEQUIL) on unconstrained problems, thirty-four multidimensional optimization test instances comprised of unimodal and multimodal benchmark problems have been solved, and respective performances are verified against those obtained from well-reputed new emerged metaheuristic algorithms. A comprehensive comparison based on the decisive metrics, including statistical analysis, performance index analysis, scalability tests, diversity analysis, and convergence rates demonstrates the effectiveness of the hybrid search methodology. Later, fourteen real-world constrained engineering problems with varying complexities were solved by the proposed DEQUIL method. The prediction performance of DEQUIL is compared with a wide range of available literature optimizers to scrutinize the improvements in problem-solving capabilities and seen that it can successfully cope with the complex constrained design problems outperforming the majority of the compared algorithm in most design cases.
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    Q-learning-based hyper-heuristic framework for estimating the energy consumption of electric buses for public transport
    (Springer International Publishing, 2024) Turgut, Oguz Emrah; Turgut, Mert Sinan; Önçağ, Ali Çaglar; Eliiyi, Uğur; Eliiyi, Deniz Türsel
    This research study introduces a Q-learning enhanced hyper-heuristic framework for the accurate estimation of energy consumption rates of electric buses. Fundamentals of reinforcement learning concepts are hybridized with the integrated newly emerged metaheuristic methods of Aquila optimizer, Barnacles Mating Optimizer, Gradient-based Optimizer, Harris Hawks Optimization, and Poor and Rich Optimization algorithms to solve high-dimensional optimization problems with higher accuracy. In this context, the Q-learning algorithm is considered a high-level heuristic for administering the selection and move acceptance mechanisms, while search agents of those mentioned above low-level competitive metaheuristic algorithms meticulously explore the search space to find the optimum global point. Q-learning guides the operating hyper-heuristic in selecting the suitable low-level optimizer based on the Q-table score during iterations. An intelligent control mechanism is devised to get a reward or penalty for the actions of the low-level algorithms. The proposed method is evaluated on thirty-two optimization benchmark problems composed of unimodal and multimodal test functions. Then, each constituent algorithm and the hyper-heuristic model are applied to thirty-dimensional benchmark functions of CEC 2017 and twenty-eight test instances of CEC 2013. Four different challenging, complex real-world engineering design cases are also solved to assess the predictability of the proposed method on constrained problems. Finally, the proposed hyper-heuristic is employed to derive the fuel consumption estimates of electric buses. It is seen that the Multiple linear regression model, whose unknown parameters are extracted by the hyper-heuristic framework, gives the best predictions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
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    Q-learning-based metaheuristic algorithm for thermoeconomic optimization of a shell-and-tube evaporator working with refrigerant mixtures
    (Springer, 2023) Turgut, Oguz Emrah; Turgut, Mert Sinan; Kirtepe, Erhan
    This research study proposes a Q-learning-based metaheuristic algorithm framework for thermal design optimization of a shell-and-tube evaporator operating with different refrigerant mixtures, which is a highly complex real-world design problem and has not been investigated yet, in previous literature approaches before. The proposed method, called QL-HEUR, uses Q-learning as a high-level heuristic to iteratively guide the competitive recently emerged low-level metaheuristic algorithms. QL-HEUR is applied to 32 unconstrained optimization benchmark functions, and results are evaluated in statistical analysis. Moreover, three multidimensional constrained optimization problems will be solved. Respective solutions unravel that QL-HEUR is very effective in finding optimum solutions to constrained and unconstrained optimization problems. QL-HEUR is employed on the design optimization of a shell-and-tube heat exchanger running with different mixture pairs as a challenging real-world benchmark case. For the design case in which R134a-R1234yf (0.8:02) mixture is considered, 8.71% of the total cost is saved compared to the preliminary design of a heat exchanger operated with pure R1234yf refrigerant. For the second design case, the application of QL-HEUR results in a decrease of 8.93% for refrigerant composition R32-R134a (0.6:0.4) in comparison with the configuration running with pure R134a. It is also seen that the heat exchanger configuration running with pure R32 refrigerant yields the lowest total cost compared to the cases accomplished by varying mixture ratios of R290 and R32. It can be concluded that the optimum configuration of the heat exchanger operated with a refrigerant mixture can be conveniently employed for minimum total cost and global warming potential.
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    Quasi-dynamic opposite learning enhanced Runge-Kutta optimizer for solving complex optimization problems
    (Springer Heidelberg, 2024) Turgut, Oguz Emrah; Turgut, Mert Sinan
    The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various branches of scientific fields. However, there is still much room for improvement as it suffers from premature convergence resulting from inefficient search space exploration. To overcome this algorithmic drawback, this research study proposes a brand-new quasi-dynamic opposition-based learning (QDOPP) mechanism to be implemented in a standard Runge-Kutta optimizer to eliminate the local minimum points over the search space. Enhancing the asymmetric search hyperspace by taking advantage of various positions of the current solution within the domain is the critical novelty to enrich general diversity in the population, significantly improving the algorithm's overall exploration capability. To validate the effectivity of the proposed RUNGE-QDOPP method, thirty-four multidimensional optimization benchmark problems comprised of unimodal and multimodal test functions with various dimensionalities have been solved, and the corresponding results are compared against the predictions obtained from the other opposition-based learning variants as well as some state-of-art literature optimizers. Furthermore, six constrained engineering design problems with different functional characteristics have been solved, and the respective results are benchmarked against those obtained for the well-known optimizers. Comparison of the solution outcomes with literature optimizers for constrained and unconstrained test problems reveals that the proposed QDOPP has significant advantages over its counterparts regarding solution accuracy and efficiency.