hybrid algorithm
در نشریات گروه علوم پایه-
Graph coloring is a crucial area of research in graph theory, with numerous algorithms proposed for various types of graph coloring, particularly graph p-distance coloring. In this study, we employ a recently introduced graph coloring algorithm to develop a hybrid algorithm approximating the chromatic number p-distance, where $p$ represents a positive integer number. We apply our algorithm to molecular graphs as practical applications of our findings.Keywords: P-Distance Coloring, P-Distance Chromatic Number, Graph Adjacency Matrix, Hybrid Algorithm
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This paper develops an efficient non-dominated sorting genetic algorithm (NSGA-II) to solve the redundancy allocation problem (RAP) of series-parallel systems. The system consists of subsystem in series, where components are used in parallel for each subsystem. Both the system and its subsystems can only take two states of complete perfect and complete failure. Identical redundant components are included to achieve a desirable reliability. The components of each subsystem, which are chosen from a list that is available in the market, are characterized by their cost, weight, and reliability. To find the optimum combination of the number of components for each subsystem, the mathematical formulation for the maximal reliability and minimal cost of the system configuration under cost constraint is first obtained. Then, a modified NSGA-II is proposed to solve the model. In this algorithm, a heuristic method of generating a primary solution is integrated to achieve better solutions. Moreover, design of experiment approach is employed to calibrate the parameters of the algorithm. At the end, some numerical examples are used to validate the solution, to assess the performance of the proposed methodology under different configurations, and to compare the performance with the ones of two other meta-heuristic algorithms. The results of experiments are generally in favor of the proposed solution algorithm.
Keywords: Reliability, Redundancy allocation problem, series-parallel systems, heuristic methods, hybrid algorithm -
In this study, solubility and tie-line data of ternary system water + glycerol + 1-butanol were determined at 293.2, 298.2, and 303.2 K and atmospheric pressure. This thermodynamic system is relevant for the production and purification of biofuels. Phase equilibrium data have been determined by the cloud-point titration method and the tie-lines were obtained by correlating the refractive index of the binodal curves as a function of mixture composition. All measured LLE data were modeled by UNIQUAC and NRTL activity coefficient equations obtaining a satisfactory accuracy with modeling errors lower than 0.4%. Binary interaction parameters of tested thermodynamic models were estimated to predict the value of tie lines using a hybrid bio-inspired swarm intelligence optimization algorithm, that is, MAKHA without and with closure equation. This hybrid method was reliable to solve the global optimization problem for the binary interaction parameter identification of this ternary system. The results of this paper provide useful information for the design and modeling of industrial units for glycerol recovery, which is a relevant industrial feedstock.Keywords: Liquid-liquid equilibria, Glycerol, MAKHA, Closure equation, hybrid algorithm
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Particle swarm optimization (PSO) is one of the practical metaheuristic algorithms which is applied for numerical global optimization. It benefits from the nature inspired swarm intelligence, but it suffers from a local optima problem. Recently, another nature inspired metaheuristic called Symbiotic Organisms Search (SOS) is proposed, which doesn't have any parameters to set at start. In this paper, the PSO and SOS algorithms are combined to produce a new hybrid metaheuristic algorithm for the global optimization problem, called PSOS. In this algorithm, a minimum number of the parameters are applied which prevent the trapping in local solutions and increase the success rate, and also the SOS interaction phases are modified. The proposed algorithm consists of the PSO and the SOS phases. The PSO phase gets the experiences for each appropriate solution and checks the neighbors for a better solution, and the SOS phase benefits from the gained experiences and performs symbiotic interaction update phases. Extensive experimental results showed that the PSOS outperforms both the PSO and SOS algorithms in terms of the convergence and success rates.
Keywords: PSO, SOS, Meta-Heuristic Optimization, Hybrid Algorithm -
مطلعات جغرافیایی که به منظور برنامه ریزی شهری ، اقلیم شناسی ، ساخت و ساز عمرانی و همچنین کشاورزی یک منطقه صورت می گیرد به تخمین درجه حرارت خاک آن منطقه وابسته می باشد . در این پژوهش دمای 50 سانتی متری عمق خاک با استفاده از مدل الگوریتم هیبریدی شبیه سازی تبرید و مدل منفرد ماشین بردار پشتیبان در ایستگاه هواشناسی شهر آدنا واقع در کشور ترکیه شبیه سازی گردید .الگوریتم شبیه سازی تبرید (SA) یک الگوریتم بهینه سازی است که وقتی با ماشین بردار پشتیبان (SVM) تلفیق می شود باعث کمینه شدن خطای تخمین و در نهایت بهبود نتایج تخمین می-شود، که در این پژوهش برای اولین بار از الگوریتم هیبریدی شبیه سازی تبرید بر پایه ماشین بردار (SVM-SA) در تخمین دمای خاک استفاده شده است، تا دقت این الگوریتم در این زمینه مورد بررسی قرار گیرد. نتایج نشان داد که مدل هیبریدی شبیه سازی تبرید بر پایه ماشین بردار پشتیبان با معیار نش- ساتکلیف حدود 0/986 و جذرمیانگین مربعات خطا برابر با 0/86 درجه سانتی گراد به خوبی برآورد می نماید. همچنین برای مدل منفرد ماشین بردار پشتیبان میزان معیار نش- ساتکلیف حدود 0/982 و جذرمیانگین مربعات خطا برابر با 0/96 درجه سانتی گراد به دست آمده است. بهترین ترکیب ورودی از بین ترکیب های تعیین شده برای تخمین دمای خاک ترکیب متشکل از ورودی های متوسط دمای هوا ، بارش ، تابش خورشیدی ، فشار هوا ، رطوبت نسبی هوا و سرعت بادانتخاب شد. نتایج نشان داد که الگوریتم هیبریدی شبیه سازی تبرید می تواند به عنوان ابزاری کارامد در مدلسازی دمای اعماق خاک استفاده شود.کلید واژگان: اقلیم شناسی، الگوریتم هیبریدی، دمای خاک، شبیه سازی، ماشین بردار پشتیبانNivar, Volume:43 Issue: 104, 2019, PP 1 -12Studies of geography to urban planning, climatology, civil construction and agriculture also conducted in an area to estimate soil temperature is dependent on the area. In this study, at 50 cm soil depth by using simulated annealing hybrid algorithm and Support Vector Machine single model in the city of Adana in Turkey weather station was simulated. The results showed that the hybrid model simulation of refrigeration is based on vector support machine with 0.986 nash-sutcliuf about criteria, the absolute error mean square error equal to 0.732 and root mean square error is equal to 0.86 well estimated. As well as single machine backup rate vector model for benchmarking Nash-sutcliuf about 982.0 and the absolute error mean square error equal to 0.793 and root mean square error is equal to 0.96 is obtained. The results showed that hybrid algorithm simulation of refrigeration could be fitted as a means of efficiently in deep soil temperature modeling.Keywords: Climatology, Hybrid algorithm, Soil temperature, Simulation, Support Vector Machine
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BackgroundThe recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth.ObjectivesIn this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies.Materials And MethodsSigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli. Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning (i.e.,c and σ) of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE).ResultsApplying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method.ConclusionApplication of the NSGA-II-LSSVM hybrid algorithm to predict precise values of c and σ parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.Keywords: Bacterial growth curve, Hybrid algorithm, LSSVM, NSGA-II, Modeling
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Let a graph G = (V;E) be given. In the path center problem we want to find a path P in G such that the maximum weighted distance of P to every vertex in V is minimized. In this paper a genetic algorithm and a hybrid of genetic and ant colony algorithms are presented for the path center problem. Some test problems are examined to compare the algorithms. The results show that for almost all examples the hybrid method results better solutions than genetic algorithm.Keywords: Genetic algorithm, Ant colony, Location theory, Path center, Hybrid algorithm
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