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Öğe Artificial intelligence approaches to estimate the transport energy demand in Turkey(Springer Heidelberg, 2021) Turgut, Mert Sinan; Eliiyi, Uğur; Turgut, Oğuz Emrah; Öner, Erdinç; Eliiyi, Deniz TürselIn this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.Öğe Chaotic quasi-oppositional arithmetic optimization algorithm for thermo-economic design of a shell and tube condenser running with different refrigerant mixture pairs(Springer London Ltd, 2022) Turgut, Mert Sinan; Turgut, Oğuz Emrah; Abualigah, LaithThis theoretical research study proposes a novel Chaotic Quasi-Oppositional Arithmetic Optimization Algorithm (COAOA) for thermo-economic optimization of a shell and tube condenser working with refrigerant mixtures. Arithmetic Optimization Algorithm (AOA) is a recently emerged metaheuristic algorithm considering different mathematical operators to optimize the candidate solutions over a wide range of search domains. The effectiveness the COAOA is assessed by applying it to a set of benchmark optimization problems and comparing the obtained solutions with that of the original AOA and its quasi-oppositional variant. The COAOA has been employed to acquire the minimum value of the total annual cost of the shell and tube condenser by iteratively varying nine decision variables of mass flow rate, shell diameter, the tube inside diameter, tube length, number of tube passes, tube layout, tube pitch ratio, the total number of baffles, and diameter ratio. Three different case studies are solved using different refrigerant pairs used for in-tube flow to show the proposed metaheuristic optimizer's efficiency and effectivity on real-world mixed-integer optimization problem. Optimal results retrieved for different mixture pairs with varying mass fractions are compared with each other, and parametric configuration yielding the minimum total cost is decided. Finally, a comprehensive sensitivity analysis is performed to investigate the influences of the design variables over the considered problem objective. Overall analysis results indicate that COAOA can be an excellent optimizer to obtain a shell and tube condenser's optimal configuration within a reasonable computation time.Öğe Differential evolution based global best algorithm: an efficient optimizer for solving constrained and unconstrained optimization problems(Springer International Publishing Ag, 2020) Turgut, Mert Sinan; Turgut, Oğuz EmrahThis study proposes an optimization method called Global Best Algorithm for successful solution of constrained and unconstrained optimization problems. This propounded method uses the manipulation equations of Differential Evolution, dexterously combines them with some of the perturbation schemes of Differential Search algorithm, and takes advantages of the global best solution obtained on the course of the iterations to benefit the productive and feasible in the search span through which the optimum solution can be easily achieved. A set of 16 optimization benchmark functions is then applied on the proposed algorithm as well as some of the cutting edge optimizers. Comparative study between these methods reveals that GBEST has the ability to achieve more competitive results when compared to other algorithms. Effects of algorithm parameters on optimization accuracy have been benchmarked with some high-dimensional unimodal and multimodal optimization test functions. Five real world design problems accompanied with three challenging test functions have been solved and verified against the literature approaches. Optimal solution obtained for economic dispatch problem also proves the applicability of the proposed method on multidimensional constrained problems with having large solution spaces.Öğe Eagle strategy based on modified barnacles mating optimization and differential evolution algorithms for solving transient heat conduction problems(Ismail Saritas, 2021) Turgut, Mert Sinan; Turgut, Oğuz EmrahSolving time-dependent heat conduction problems through a conventional solution procedure of iterative root-finding method may sometimes cause difficulties in obtaining accurate temperature distribution across the heat transfer medium. Analytical root-finding methods require good initial estimates for finding exact solutions, however locating these promising regions is some kind of a black-box process. One possible answer to this problem is to convert the root-finding equation into an optimization problem, which eliminates the exhaustive process of determining the correct initial guess. This study proposes an Eagle Strategy optimization framework based on modified mutation equations of Barnacles Mating Optimizer and Differential Evolution algorithm for solving one-dimensional transient heat conduction problems. A test suite of forty optimization benchmark problems have been solved by the proposed algorithm and the respective solution outcomes have been compared with those found by the reputed literature optimizers. Finally, a case study associated with a transient heat conduction problem have been solved. Results show that Eagle strategy can provide efficient and feasible results for various types of solution domains. © 2021, Ismail Saritas. All rights reserved.Öğe Global best-guided oppositional algorithm for solving multidimensional optimization problems(Springer, 2020) Turgut, Mert Sinan; Turgut, Oğuz EmrahThis paper presents an alternative optimization algorithm to the literature optimizers by introducing global best-guided oppositional-based learning method. The procedure at hand uses the active and recent manipulation schemes of oppositional learning procedure by applying some modifications to them. The first part of the algorithm deals with searching the optimum solution around the current best solution by means of the ensemble learning-based strategy through which unfeasible and semi-optimum solutions have been straightforwardly eliminated. The second part of the algorithm benefits the useful merits of the quasi-oppositional learning strategy to not only improve the solution diversity but also enhance the convergence speed of the whole algorithm. A set of 22 optimization benchmark functions have been solved and corresponding results have been compared with the outcomes of the well-known literature optimization algorithms. Then, a bunch of parameter estimation problem consisting of hard-to-solve real world applications has been analyzed by the proposed method. Following that, eight widely applied constrained benchmark problems along with well-designed 12 constrained test cases proposed in CEC 2006 session have been solved and evaluated in terms of statistical analysis. Finally, a heat exchanger design problem taken from literature study has been solved through the proposed algorithm and respective solutions have been benchmarked against the prevalent optimization algorithms. Comparison results show that optimization procedure dealt with in this study is capable of achieving the utmost performance in solving multidimensional optimization algorithms.Öğe Improved artificial cooperative search algorithm for solving non- convex economic dispatch problems with valve-point effects(2018) Turgut, Oğuz EmrahThis paper presents Improved Artificial Cooperative Search (IACS) algorithm for solving economic dispatch problemsconsidering the valve point effects, ramp rate limits, transmission losses and prohibited operation zones. In order to improve the solutionquality and increase the search efficiency, a novel perturbation scheme called “Global best guided chaotic local search” is proposed andincorporated into ACS algorithm. The effectiveness of the proposed IACS algorithm has been benchmarked with twelve widely knownoptimization test problems. In order to assess the performance of the proposed algorithm on non-convex optimization problems, four casestudies related to highly nonlinear economic dispatch problems have been solved . Results retrieved from IACS algorithm have beencompared with literature approaches in terms of minimum, maximum and average generation cost values. Comparison results indicate thatIACS produces more economical power load than those of other optimizers available in the literature.Öğe Island-based Crow Search Algorithm for solving optimal control problems(Elsevier, 2020) Turgut, Mert Sinan; Turgut, Oğuz Emrah; Eliiyi, Deniz TürselCrow Search Algorithm (CROW) is one of the members of recently developed swarm-based meta-heuristic algorithms. Literature includes different applications of this algorithm on engineering design problems. However, this optimization method suffers from some drawbacks such as premature convergence and trapping into local optima at the early phase of iterations. In order to conquer this algorithm specific inabilities, many research studies have been conducted in the literature dealing with the improvements and enhancements on the search mechanism of CROW. Structured population mechanism plays a vital role in preserving and controlling diversity, and thus increases the solution efficiency in evolutionary algorithms. Among the different types of methods used in structured algorithms, the island model is one of the widely applied solution strategies, in which the population individuals are subdivided into a predefined number of subpopulations. Migration mechanism is the key factor increasing population diversity, which takes place between independently running subpopulations during iterations to exchange valuable and useful solution information. This study embeds the fundamentals of the island model concepts into the Crow Search Algorithm to improve its probing capabilities of the search domain, by means of the periodically interacting subpopulations on the course of iterations. In addition, four different hierarchical migration topologies have been proposed, and their search effectiveness have been evaluated and compared over 45 optimization test functions. The optimization function test set includes classic benchmark optimization problems and CEC 2015 benchmark functions. Furthermore, each hierarchical island model is applied for solving six different optimal control problems in order to investigate their efficiencies on multi-dimensional real world optimization problems. The investigated optimal control problems are parallel reaction, continuous stirred tank reactor, batch reactor consecutive reaction, nonlinear constrained mathematical system, nonlinear continuous stirred tank reactor and nonlinear crane container problems. It is found out that the island model concepts improved the optimization performance of CROW. The proposed island models outperformed or showed similar performance compared to the six selected literature optimizers for 27-29 classic benchmark optimization problems. Moreover, incorporating the master sub-population to the island model improved the optimization capability of the algorithm further in most cases. The island models that employ the master sub-population came up with more favorable results compared to their non-master sub-population peers in all optimal control problems. The island model that includes the master sub-population and has the migration topology entitled 82'' found the most desirable solutions for 4-6 optimal control problems. (C) 2020 Elsevier B.V. All rights reserved.Öğe Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems(Elsevier, 2023) Turgut, Oğuz Emrah; Turgut, Mert SinanAquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.Öğe Multi-objective particle swarm optimization of the k-type shell and tube heat exchanger (case study)(Yildiz Technical University, 2021) Nadi M.; Ehyaei M.A.; Ahmadi A.; Turgut, Oğuz EmrahThis paper investigated optimization of two objectives function include the total amount of heat transfer between two mediums and the total cost of shell and tube heat exchanger. The study was carried out for k-type heat exchanger of the cryogenic unit of gas condensates by multiple objective particle swarm optimization. Six decision variables including pipe pitch ratio, pipe diameter, pipe number, pipe length, baffle cut ratio, and baffle distance ratio were taking into account to conduct this simulation-based research. The results of mathematical modeling confirmed the actual results (data collected from the evaporator unit of the Tehran refinery’s absorption chiller). The optimization results revealed that the two objective functions of heat transfer rate and the total cost were in contradiction with each other. The results of the sensitivity analysis showed that with change in the pitch ratio from 1.25 to 2, the amount of heat transfer was reduced from 420 to 390 kW about 7.8%. Moreover, these variations caused reduction in cost function from 24,500 to 23,500 $, less than 1%. On the other hand, an increase in pipe length from 3 to 12 meters, the heat transfer rate raised from 365 to 415 kW by 13.7%, while the cost increased from 20,000$ to 24500$ about 22%. © 2021. All rights reserved.Öğe A New saturated two-phase flow boiling correlation based on propane (R290) data(Springer Heidelberg, 2021) Turgut, Oğuz Emrah; Çoban, Mustafa TurhanThere are plenty of literature research studies investigating two-phase heat transfer characteristics of propane under varying operational conditions. Based on the collected data retrieved from the experimental measurements, several flow boiling heat transfer correlations have been proposed up to now. However, the prediction accuracy of the proposed correlations for propane refrigerant is still in question as most of the correlation is developed for their measurements or derived for a limited range of operational conditions. To conquer this drawback, this study proposes a new flow boiling heat transfer model for smooth tubes based on a propane experimental database compiled of 2179 points obtained from different eighteen laboratories around the world. Operational conditions of the database cover mass fluxes between 50 and 600 kg/m(2)s, saturation temperatures between - 35.0 and 43.0 degrees C, heat fluxes between 2.5 and 227.0 kW/m(2), hydraulic diameters between 0.3 and 7.7 mm, and thermodynamic qualities 0.01 to 0.99. Estimations performed by the new flow boiling model have been compared to those obtained by the literature correlations, and comparative results indicate that the proposed model surpasses the existing flow boiling in terms of prediction accuracy with a mean absolute error of 19.1% and mean relative error of 1.7%.Öğe A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler(Springer International Publishing Ag, 2021) Turgut, Oğuz EmrahThis research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.Öğe A novel Master-Slave optimization algorithm for generating an optimal release policy in case of reservoir operation(Elsevier, 2019) Turgut, Mert Sinan; Turgut, Oğuz Emrah; Afan, Haitham Abdulmohsin; El-Shafie, AhmedDams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master-slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master-slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand.Öğe Novel saturated flow boiling correlations for R600a and R717 refrigerants(Taylor & Francis Inc, 2021) Turgut, Oğuz Emrah; Genceli, Hadi; Asker, Mustafa; Çoban, Mustafa TurhanThis research study provides a detailed analysis of the two-phase flow boiling heat transfer coefficients of R600a and R717. In addition, it proposes novel saturated flow boiling heat transfer correlations for these two refrigerants. In this context, 1306 experimental data points for R600a and 885 data samples for R717 are extracted from the literature works to develop new flow boiling models. Two different correlations are constructed under different forms of baseline frameworks for each refrigerant. The proposed flow boiling model for R600a utilizes the dimensionless numbers and operational parameters of the best performing literature correlations for this refrigerant while the model developed for R717 takes the advantage of piecewise continuous correlation form which successfully trace the tendencies of heat transfer coefficient with varying vapor qualities. It is found that the new model for R600a has a mean absolute error of 12.4% and mean relative error of 0.1, predicting 78.5% of the entire database within +/- 20.0% error band whereas the proposed correlation for R717 has an absolute error value of 17.3% having 65.3% of the data within +/- 20.0% error band, which are much better and accurate estimations compared to those obtained by the existing flow boiling models for R717 refrigerant.Öğe An oppositional Salp Swarm: Jaya algorithm for thermal design optimization of an Organic Rankine Cycle(Springer International Publishing Ag, 2021) Turgut, Mert Sinan; Turgut, Oğuz EmrahThis study proposes a hybrid metaheuristic algorithm to tackle both single and multi objective optimization problems that are subjected to hard constraints.Twenty-four single objective optimization benchmark problems comprising unimodal and multi modal test functions have been solved by the proposed hybrid algorithm (OPSSAJ) and numerical results have been compared with those acquired by some of the new emerged metaheuristic optimizers. The proposed OPSSAJ shows a significant accuracy and robustness in most of the cases and proves its efficiency in solving high dimensional problems. As a real-world case study, seventeen operational design parameters of an organic rankine cycle (ORC) operating with a binary mixture of R227EA and R600 refrigerants are optimized by the proposed hybrid OPSSAJ to obtain the optimum values of contradicting dual objectives of second law efficiency and Specific Investment Cost. A Pa reto curve composed of non-dominated solutions is constructed through the weighted sum method and the final solution is chosen by the reputed TOPSIS decision-maker. The pareto curve and best-compromising result obtained by utilizing the OPPSAJ are compared with that of acquired by using nondominated sorting genetic algorithm II (NSGA-II) and multiple objective particle swarm optimization (MOPSO) algorithms. The multi-objective ORC design obtained with the OPSSAJ yields a significant improvement in thermal efficiency and cost values compared to designs found by the NSGA-II and MOPSO algorithms. Furthermore, a sensitivity analysis is performed to observe the influences of the selected design variables on problem objectives.Öğe Solving time-dependent heat conduction problems using metaheuristic algorithms extended with a novel local search strategy(Springer Int Publ Ag, 2021) Turgut, Oğuz EmrahThis study proposes a novel and dexterous local search scheme for improving the exploitation phase of a generic metaheuristic algorithm. The proposed local search considers a twofold probing mechanism, which takes advantage of a chaotic number generated by the hybrid chaotic map composed of Logistic map and Kent map to move around the so-far-obtained global best solutions to reach feasible candidate solutions. Also, an iterative local search scheme inspired by a variant of the differential evolution algorithm is incorporated into the proposed manipulation scheme to enhance intensification on the promising regions. The proposed scheme is included in the well-reputed metaheuristics of differential evolution, crow search, whale optimization, and sine-cosine algorithms to assess the resulting improvements made on the optimization accuracy. Forty optimization benchmark functions composed of unimodal and multimodal test problems have been solved by the local search improved and basic forms of these optimizers to identify the amelioration in terms of solution accuracy and robustness. Two different real-world constrained optimization problems have been solved by these algorithms to analyze the improvement in solution qualities maintained by the utilization of the proposed local search method. Furthermore, these mentioned optimization algorithms along with their improved forms have been applied to one-dimensional transient heat conduction problems to obtain accurate temperature distribution across the heat transfer medium. Optimization results reveal that utilizing local search enhanced metaheuristic algorithms can be considered a favorable alternative to conventional solution methods for solving transient heat conduction problems.Öğe Whale optimization and sine-cosine optimization algorithms with cellular topology for parameter identification of chaotic systems and Schottky barrier diode models(Springer, 2021) Turgut, Mert Sinan; Şağban, Hüseyin Muzaffer; Turgut, Oğuz Emrah; Özmen, Özge TüzünThis research study aims to enhance the optimization accuracy of the two recently emerged metaheuristics of whale and sine-cosine optimizers by means of the balanced improvements in intensification and diversification phases of the algorithms provided by cellular automata (CA). Stagnation at the early phases of the iterations, which leads to entrapment in local optimum points in the search space, is one of the inherent drawbacks of the metaheuristic algorithms. As a favorable solution alternative to this problem, different types of cellular topologies are implemented into these two algorithms with a view to ameliorating their search mechanisms. Exploitation of the fertile areas in the search domain is maintained by the interaction between the topological neighbors, whereas the improved exploration is resulted from the smooth diffusion of the available population information among the structured neighbors. Numerical experiments have been carried out to assess the optimization performance of the proposed cellular-based algorithms. Optimization benchmark problems comprised of unimodal and multimodal test functions have been applied and numerical results have been compared with those found by some of the state-of-the-art literature optimizers including particle swarm optimization, differential evolution, artificial cooperative search and differential search. Cellular variants have been outperformed by the base algorithms for multimodal benchmark problems of Levy and Penalized1 functions. Then, the proposed cellular algorithms have been applied to two different parameter identification cases in order to test their efficiencies on real-world optimization problems. Extensive performance evaluations on different parameter optimization cases reveal that incorporating the CA concepts on these algorithms not only improves the optimization accuracy but also provides considerable robustness to acquired solutions.
