PID Tuning of Servo Motor Using Bat Algorithm

AbstractThe Proportional-Integral-Derivative (PID) controller uses three parameters to produce the desired output of a system. The desired system performances are in terms of overshoot, rise time, settling time and steady state error. This has brought about various methods to tune the controller to the desired response. Therefore, the presence of the bat algorithm as part of the system will reduce the time and cost of tuning these parameters and improve the overall system performance

NMEP based Gaussian Mutation Process on Optimizing Fitness Function for MOEED

The increment of Economic Dispatch (ED) problem is very distressing today. In view of countless of the researchers doing the research to minimize the ED problem day after day, the multi objective New Meta Heuristic Evolutionary Programming (NMEP) techniques are proposed to optimize the multi objective function in ED problem called as Multi Objective Environmental Economic Dispatch (MOEED). The techniques mimic the original Meta Heuristic Evolutionary Programming (Meta-EP) and merge with Artificial Immune System (AIS) with some improvement in Gaussian mutation process and cloning process. The NMEP produced two objective function result simultaneously by exercising the weighted sum method. In order to justify the result, the comparison between the NMEP and Meta-EP techniques is conducted with difference case number of alpha. Therefore, the outcome of the simulation shows the NMEP approach is better than Meta-EP in the both case numbers of alpha. The simulation is operated using MATLAB simulation based on standard IEEE 26 bus system in the laboratory

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

Leapfrogging with improved initialization and its applications: Demonstration of leapfrogging on horizon predictive control of a heat exchanger

Leapfrogging (LF) is a recently developed optimization technique that initially places players in random spots in the feasible decision variable space [1]. The approach to reach the global optimum is by "Leaping" the player with the worst objective function (OF) value "Over" the player with the best OF value into the reflected hyper volume that connects the player with the best and the worst OF until the players converge at an optimum [1]. LF has several advantages compared to other optimization techniques in terms of computational efficiency and higher probability to reach the global optimum [1]. This is demonstrated in several applications [1-7].The main focus of this work is to develop LF [8] by exploring the initialization step through a fundamental analysis and supporting the developed technique with mathematical truths. In this improvisation the OF surface is initially explored with a large number of players, the players are sorted in ascending order of their OF values and the top few players are selected to continue with the optimization technique. This improvement is found to increase the probability of finding the global optimum as one of the initial players is placed in the vicinity of the global optimum during initialization and thus draws all the other players towards it.In order to establish the applicability of LF and its improvement on process engineering applications, this work focusses on implementation of original and modified LF to model a pilot scale Shell and Tube Heat Exchanger (HX 001) process, which is nonlinear. Steam is used to increase the temperature of the water on the cold side. For this study, the outlet temperature of the cold side fluid is considered as the control variable (CV). The hot side steam valve opening is considered as the manipulated variable (MV). This CV-MV relation is modeled using original and modified LF to find the model parameters that best fit the experimental skyline function generated for modeling purpose in the Unit Operations Lab, OSU-Stillwater. Next, the model parameters are used to implement a horizon predictive control on the HX001 process to control the CV

Adaptiver Suchansatz zur multidisziplinären Optimierung von Leichtbaustrukturen unter Verwendung hybrider Metaheuristik

Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. Choosing the best design and production process in the development period is the most significant link in the automobile production chain. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables. This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material. An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results. Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool. The ability of such tool to present the optimum results in multi-objective problems is improved by using some user-selected fitness functions. Finally, an exemplary sub-assembly made of sheet metal parts from a car body is optimized to enhance both, static load case and crashworthiness.Die Automobilindustrie hat in den letzten Jahren unter dem Druck von Umweltvorschriften, Sicherheitsanforderungen und wettbewerbsfähigem Markt neue Wege auf dem Gebiet der Technologien eröffnet. Leichtbau gilt als eine der innovativsten und offenkundigsten Lösungen, um Umwelt- und Sicherheitsziele zu wettbewerbsfähigen Preisen zu erreichen. Die Wahl des besten Designs und Verfahrens für Produktionen in der Entwicklungsphase ist der wichtigste Ring der Automobilproduktionskette. Um unzählige Produktionsziele zu erreichen, müssen zahlreiche Design- und Prozessparameter bewertet werden. Die Anzahl und Variation der Lösungen und Ziele sowie einige Einschränkungen wie Fertigungsbeschränkungen, finanzielle Grenzen und Fristen beeinflussen die Auswahl einer guten Kombination von Variablen. In dieser Studie werden strukturelle Optimierungswerkzeuge für aus Blech gefertigte Baugruppen, z. Karosserie, basierend auf Parametrisierung und Bewertung von Lösungen in CAD bzw. CAE. Diese Methodik konzentriert sich auf die Lösungen, die dazu führen, dass die richtige Menge an leichtem / festem Material an der richtigen Stelle der Struktur verwendet wird, anstatt vollständig ersetzt zu werden. Eine adaptive Hybrid-Metaheuristik soll verhindern, dass alle Faktoren, die Bedrohungsoptimierungstools in einem lokalen Minimum konvergieren, anstelle eines globalen Optimums. Das Auffinden des globalen Optimums wird durch einige explorative und ausbeuterische Such Heuristiken gewährleistet. Die Zuverlässigkeit, Genauigkeit und Geschwindigkeit des vorgeschlagenen Algorithmus wird mit ähnlichen Algorithmen in akademischen Optimierungsproblemen validiert und führt zu respektablen Ergebnissen. Da Strukturen möglicherweise einem weiten Bereich von Lastfällen unterliegen, z. statische, zyklische, dynamische, Temperatur usw. Möglichkeit der multidisziplinären Optimierung wurde in Optimierungswerkzeugen bereitgestellt. Um die nichtlineare Reaktion von Zielen zu überwinden und um den hohen Zeitverbrauch von FEM-Analysen in Absturzereignissen zu bewältigen, könnte ein Ersatzmodell vom Benutzer verwendet werden. Die Fähigkeit von Optimierungswerkzeugen, optimale Ergebnisse bei Problemen mit mehreren Zielsetzungen zu präsentieren, wird durch die Verwendung einiger vom Benutzer ausgewählten Fitnessfunktionen verbessert. Eine Unterbaugruppe aus Blechteilen, die zur Automobilkarosserie gehören, ist optimiert, um beide zu verbessern; statischer Lastfall und Crashsicherheit

Optimal consignment stocking policies for a supply chain under different system constraints

The research aims are to enable the decision maker of an integrated vendor-buyer system under Consignment Stock (CS) policy to make the optimal/sub-optimal production/replenishment decisions when some general and realistic critical factors are considered. In the system, the vendor produces one product at a finite rate and ships the outputs by a number of equal-sized lots within a production cycle. Under a long-term CS agreement, the vendor maintains a certain inventory level at the buyer’s warehouse, and the buyer compensates the vendor only for the consumed products. The holding cost consists of a storage component and a financial component. Moreover, both of the cases that the unit holding costs may be higher at the buyer or at the vendor are considered. Based upon such a system, four sets of inventory models are developed each of which considers one more factor than the former. The first set of models allows a controllable lead-time with an additional investment and jointly determines the shipping size, the number of shipments, and the lead time, that minimize the yearly joint total expected cost (JTEC) of the system. The second set of models considers a buyer’s capacity limitation which causes some shipments to be delayed so that the arrival of these shipments does not cause the buyer’s inventory to go beyond its limitation. As a result, the number of delayed shipments is added as the fourth decision variable. A variable demand rate is allowed in the third set of models. Uncertainty caused by the varying demand are controlled by a safety factor, which becomes the fifth decision variable. Finally, the risk of obsolescence of the product is considered in the fourth model. The first model is solved analytically, whereas the rest are not, mainly because of the complexity of the problem and the number of variables being considered. Three doubly-hybrid meta-heuristic algorithms that combine two different hybrid meta-heuristic algorithms are developed to provide a solution procedure for the rest of models. Numerical experiments illustrate the solution procedures and reveal the effects of the buyer’s capacity limitation, the effects of the variable demand rate, and the effects of the risk of obsolescence, on the system. Furthermore, sensitivity analysis shows that some of the system parameters (such as the backorder penalty, the extra space penalty, the ratio of the unit holding cost of the vendor over that of the buyer) are very influential to the joint system total cost and the optimal solutions of the decision variables

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Güncel en iyileme algoritmalarının paralel ve birlikte uygulamaları ve performans analizleri

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.En iyileme yöntemleri yapılan işin en iyi yapılmasını sağlamak için kullanılırlar. Bu tekniklerin kullanılmasındaki temel hedef her zaman için en iyi çözümleri yakalayabilmektir. Uygunluk veya hata değeri tanımlanabilen her sistemin en iyi çözümünün elde edilmesinde en iyileme algoritmaları kullanılabilir. Sadece ait oldukları problemlere özgü olmaları ve yüksek hesaplama maliyeti içermeleri gibi sebepler nedeniyle mevcut geleneksel en iyileme algoritmalarının kullanımı çok sayıda parametre içeren gerçek dünya problemlerinin çözümünde bazen yeterli olmayabilir. Bu gibi durumlarda daha az işlem ile daha kısa sürede en iyi çözüme yakınsayabilen meta-sezgisel yöntemlerin kullanımı daha makul çözümler olarak karşımıza çıkmaktadır. Son 20 yıl içerisinde doğadan ilham alınarak çok sayıda meta-sezgisel en iyileme algoritması geliştirilmiştir. Buna paralel olarak bazı araştırmacılar mevcut algoritmalar üzerinde birtakım iyileştirmeler yapmışlar, bazıları da birden fazla algoritmayı bir arada kullanarak performansı daha yüksek melez yöntemler elde etmişler ve daha sonra bu yöntemleri kullanarak gerçek dünya problemlerine en iyi çözümler üretmişlerdir. Bu tez çalışmasında sistem kimliklendirme süreci, yapay sinir ağı eğitimi, sempozyum katılımcı listelerinin düzenlenmesi, slab kesme uzunluklarının planlanması gibi gerçek dünyaya ait problemlere birer en iyileme problemi olarak yaklaşılmış, seçilen güncel ve yaygın meta-sezgisel algoritmalar kullanılarak geleneksel yöntemlerin çözümleri ile rekabet edebilen çözümler üretilmiştir. Ayrıca, karar ağacı tasarım süreci hem kombinatoryal hem de nümerik en iyilemeleri içeren bir problem olarak ele alınmış, olası karar ağacı tasarımları arasında sistematik arama yapan yeni bir yöntem ile karar ağacı tasarımı gerçekleştirilmiştir. Önerilen yöntemle elde edilen test sonuçlarının aynı veri setinin kullanıldığı daha önceki karar ağacı çalışmaları ile elde edilen sonuçlardan daha iyi olduğu görülmüştür. Son olarak, yapay arı koloni ve göçmen kuşlar en iyileme algoritmaları kullanılarak yeni modifiye, melez ve paralel çalışma sistematikleri önerilmiştir. Önerilen yöntemlerin performans testlerinden elde edilen sonuçlar, onların daha iyi keşif ve yakınsama yeteneklerine sahip olduklarını ortaya koymuştur.Optimization methods are employed in order to make a job in an optimal way. The main aim of their usage is to get an optimal solution in every execution. Optimization algorithms can be applied to find optimal solutions for the systems whose fitness or error calculations can be defined. Sometimes, existing conventional optimization algorithms may be insufficient for the real world problems having many parameters because of the reason that they are problem specific and have higher calculation costs. Since metaheuristic algorithms can find near optimal solutions with less calculations requiring lower time, their usages seem more feasible for these cases. Within the past 20 years, so many metaheuristic algorithms which are inspired by the nature have been developed by researchers. In parallel to these studies, while some of the researchers were working on some enhancements for existing algorithms, some of them were working on their hybrid forms. Then, they tried to find more optimal solutions for real world problems by using these new enhanced and hybrid algorithms. In this dissertation study, some real world problems such as system identification process, artificial neural network training, preparation of symposium attendee lists, scheduling slab cutting lengths etc. are thought to be optimization problems. Some competitive solutions with respect to solutions of the conventional methods are generated to these real world problems by using some recent and common metaheuristic algorithms. In addition, thinking the decision tree construction process as a problem including both numerical and combinatorial optimizations, a novel decision tree construction method which makes a systematic search among possible decision tree designs is proposed to get optimal decision tree. It is seen that the results obtained by proposed method are better than those of previous studies using same data set. Finally, some modified, hybrid and parallel running strategies using artificial bee colony and migrating birds optimization algorithms are proposed. It is observed from the performance test results that proposed strategies have better exploration and exploitation capabilities