Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics

Design optimisation and real-time energy management control of the electrified off-highway vehicle with artificial intelligence

Targeting zeros-emissions in transportation, future vehicles will be more energy-efficient via powertrain electrification. This PhD research aims to optimise an electrified off-highway vehicle to achieve the maximum energy efficiency by exploring new artificial intelligence algorithms. The modelling study of the vehicle system is firstly performed. Offline design optimisation and online optimum energy management control methodologies have been researched. New optimisation methods are proposed and compared with the benchmark methods. Hardware-in-the-Loop testing of the energy management controller has been carried out for validation of the control methods. This research delivers three original contributions: 1) Chaos-enhance accelerated particle swarm optimisation algorithm for offline design optimisation is proposed for the first time. This can achieve 200% higher reputation-index value compared to the particle swarm optimisation method. 2) Online swarm intelligent programming is developed as a new online optimisation method for model-based predictive control of the vehicle energy-flow. This method can save up to 17% energy over the rule-based strategy. 3) Multi-step reinforcement learning is researched for a new concept of ‘model-free’ predictive energy management with the capability of continuously online optimisation in real-world driving. It can further save at least 9% energy

New trends in electrical vehicle powertrains

The electric vehicle and plug-in hybrid electric vehicle play a fundamental role in the forthcoming new paradigms of mobility and energy models. The electrification of the transport sector would lead to advantages in terms of energy efficiency and reduction of greenhouse gas emissions, but would also be a great opportunity for the introduction of renewable sources in the electricity sector. The chapters in this book show a diversity of current and new developments in the electrification of the transport sector seen from the electric vehicle point of view: first, the related technologies with design, control and supervision, second, the powertrain electric motor efficiency and reliability and, third, the deployment issues regarding renewable sources integration and charging facilities. This is precisely the purpose of this book, that is, to contribute to the literature about current research and development activities related to new trends in electric vehicle power trains.Peer ReviewedPostprint (author's final draft

Roles of polymorphisms and expression of genes coding for chemokines CX3C ligand 1 and CXC ligand 16 and their receptors in the development and progression of multiple sclerosis in Serbia

Multipla skleroza je hroniĉna inflamatorna, autoimunska, demijelinizaciona i neurodegenerativna bolest centralnog nervnog sistema (CNS-a). Hemokini i njihovi receptori predstavljaju znaĉajne medijatore inflamacije koji uĉestvuju u patogenezi odreĊenih hroniĉnih inflamatornih i autoimunskih bolesti meĊu kojima je i multipla skleroza. Ciljni hemokini u ovoj studiji, CX3C ligand 1 (CX3CL1) i CXC ligand 16 (CXCL16), specifiĉni su po tome što postoje u dve forme - kao transmembranski adhezivni molekuli i kao solubilni hemoatraktanti koji nastaju nakon proteolitiĉkog seĉenja vanćelijskih hemokinskih domena njihovih transmembranskih formi. U toku inflamatornog odgovora, na membrani endotelnih vaskularnih ćelija eksprimirani su CX3CL1 i CXCL16, a na membrani leukocita receptori za CX3CL1 (CX3CR1) i CXCL16 (CXCR6), te ovi hemokini i njihovi receptori posreduju u prodiranju leukocita iz krvi u tkivo zahvaćeno inflamacijom, podsticanjem hemotaksije i adhezije leukocita za aktivirani endotel krvnog suda. Ova studija obuhvata genetsko-epidemiološku analizu polimorfizama zamena pojedinaĉnih nukleotida u kodirajućim regionima gena, koje rezultuju zamenama aminokiselina. To su polimorfizmi V249I i T280M u genu za CX3CR1, i I123T i A181V u genu za CXCL16. U prethodnim studijama je pokazano da ovi genski polimorfizmi menjaju funkcionalna svojstva CX3CR1 i CXCL16, kao i da su asocirani sa patogenezom odreĊenih hroniĉnih inflamatornih bolesti. Uzimajući to u obzir, ova studija je imala za cilj da po prvi put ispita asocijaciju navedenih polimorfizama u genima za CX3CR1 i CXCL16 sa nastankom i progresijom multiple skleroze. Primenom alel-specifiĉne PCR metode i PIRA PCR-RFLP metode detektovani su genotipovi polimorfizama V249I i T280M u genu za CX3CR1, kod zdravih kontrola i pacijenata sa multiplom sklerozom. UtvrĊeno je da haplotip I249T280 u genu za CX3CR1 ima znaĉajno veću uĉestalost kod pacijenata sa relapsno-remitentnom (RR) formom, u odnosu na pacijente sa sekundarno-progresivnom (SP) formom multiple skleroze, što znaĉi da ovaj haplotip ima protektivni efekat na progresiju RR u SP formu bolesti...Multiple sclerosis is a chronic inflammatory, autoimmune, demyelinating and neurodegenerative disease of the central nervous system (CNS). Chemokines and their receptors are important mediators of inflammation, which are involved in pathogenesis of certain chronic inflammatory and autoimmune diseases including multiple sclerosis. Chemokines of interest in this study, CX3C ligand 1 (CX3CL1) and CXC ligand 16 (CXCL16), are specific in that they can exist either as transmembrane adhesion molecules or soluble chemoattractants being generated by proteolytic cleavage of their transmembrane forms’ extracellular domains. During the inflammatory response, CX3CL1 and CXCL16 are expressed on the surface of vascular endothelium, while the leukocytes produce membrane receptors for CX3CL1 (CX3CR1) and CXCL16 (CXCR6). Therefore, these chemokines and their receptors mediate the infiltration of leukocytes from blood into the inflamed tissue areas, by stimulation of both chemotaxis and adhesion of leukocytes to the activated endothelium of blood vessels. This study is based on genetic epidemiological analysis of single nucleotide polymorphisms, which are located in the coding regions of genes and result in amino acids’ substitutions. These are V249I and T280M substitutions in the gene coding for CX3CR1, and I123T and A181V substitutions in the gene coding for CXCL16. In previous studies these polymorphisms have been associated with the functional properties of CX3CR1 and CXCL16 as well as the pathogenesis of certain chronic inflammatory diseases. Therefore, this study aimed to investigate the association of the polymorphisms in CX3CR1 and CXCL16 genes with the development and progression of multiple sclerosis. Using the allele-specific PCR and PIRA PCR-RFLP methods, genotypes of CX3CR1 V249I and T280M polymorphisms were detected in healthy controls and patients with multiple sclerosis. Following statistical analysis showed significantly higher frequency of CX3CR1 I249T280 haplotype in patients with relapsingremitting (RR) form, compared to patients with secondary-progressive (SP) form of multiple sclerosis, so this haplotype had a protective effect on progression of RR to SP form of the disease..

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Advances in Modelling and Control of Wind and Hydrogenerators

Rapid deployment of wind and solar energy generation is going to result in a series of new problems with regards to the reliability of our electrical grid in terms of outages, cost, and life-time, forcing us to promptly deal with the challenging restructuring of our energy systems. Increased penetration of fluctuating renewable energy resources is a challenge for the electrical grid. Proposing solutions to deal with this problem also impacts the functionality of large generators. The power electronic generator interactions, multi-domain modelling, and reliable monitoring systems are examples of new challenges in this field. This book presents some new modelling methods and technologies for renewable energy generators including wind, ocean, and hydropower systems

Advances in Modelling and Control of Wind and Hydrogenerators

Rapid deployment of wind and solar energy generation is going to result in a series of new problems with regards to the reliability of our electrical grid in terms of outages, cost, and life-time, forcing us to promptly deal with the challenging restructuring of our energy systems. Increased penetration of fluctuating renewable energy resources is a challenge for the electrical grid. Proposing solutions to deal with this problem also impacts the functionality of large generators. The power electronic generator interactions, multi-domain modelling, and reliable monitoring systems are examples of new challenges in this field. This book presents some new modelling methods and technologies for renewable energy generators including wind, ocean, and hydropower systems

Multi-objective optimization of planetary gearboxes based on adaptive hybrid metaheuristic algorithms

Планетарни преносници спадају у групу механичких зупчастих преносника, који су широко заступљени за трансформацију и пренос снаге првенствено због компактности конструкције, високе поузданости и степена искоришћења. Полазећи од функције, коју треба да испуни у оквиру неке конструкције, као и све строжих захтева у погледу перформанси преносника, предмет истраживања ове докторске дисертације је развој вишекритеријумског оптимизационог модела планетарног преносника. Развијени модели који задовољавају низ строгих захтева у погледу: компактности конструкције, минимизације губитака у преносу снаге, равномерности расподеле оптерећења, као и поузданости у различитим експлоатационим условима. За постављени проблем вишекритеријумске оптимизације планетарног преносника формиране су одговарајуће критеријумске функције и дефинисан је низ функционалних ограничења, као и одговарајући домени примене свих релевантних величина зупчастих парова са спољашњим и унутрашњим озубљењем и планетарног преносника као сложеног система, у циљу несметане монтаже, поузданог рада и спрезања зупчастих парова. У оквиру докторске дисертације, развијен је одговарајући механички модел за одређивање степена искоришћења истовремено спрегнутих зупчастих парова, у зависности од њихових геометријских параметара, као и од услова подмазивања. Критеријумске функције формираног вишекритеријумског оптимизационог проблема су нелинеарне и неконвексне па се глобално оптимално решење не може нумерички одредити конвенционалним методама оптимизације. Према томе, у циљу решавања овог комплексног оптимизационог проблема потребно је применити метахеуристичке оптимизационе алгоритме. Дакле, постоји стална потреба за побољшањем постојећих и развојем нових метахеуристичких алгоритама и то развојем адаптивних техника за подешавање управљачких параметара и хибридизацијом алгоритама. У складу са тим, у оквиру докторске дисертације детаљно су разматрани метахеуристички алгоритми, који припадају групи еволутивних алгоритама, као што су алгоритам диференцијалне еволуције (Differential Evolution, DE), генетски алгоритам (Genetic Algorithm, GA) као и алгоритми инспирисани биолошким системима у природи, и то алгоритам оптимизације ројем честица (Partical Swarm Optimization, PSO). У циљу отклањања недостатака метахеуристичких алгоритама који се јављају током оптимизационог процеса, алгоритми предложени у оквиру дисертације су модификовани кроз: развој адаптивних механизама за подешавање вредности управљачких параметара, и хибридизацију адекватних алгоритама. У циљу формирања ефикасног хибридног алгоритма, процесу хибридизације је претходила детаљна нумеричка симулација и статистичка анализа перформанси разматраних алгоритама. Према томе, хибридизацијом алгоритама постигнуто је адекватно искоришћење предности једног и истовремена елиминација недостатака другог алгоритма...Planetary gearboxes have a wide application in the field of transformation and transmission of power from the drive to the working machine, due to the compact structure, high reliability and efficiency. Due to the increasingly stringent performance requirements, which planetary gearboxes must satisfy, the research in this dissertation is focused on the problem of multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm, which satisfies a number of strict requirements, such as: compact construction, minimization of power loss, load distribution and reliability in different operating conditions. In this work the formulations of the objective functions for the considered multiobjective optimization problem of planetary gearbox have been outlined along with the appropriate constraints. The formulated constraints have been analyzed and appropriate domains of practical applications of internal and external gear pairs have been formulated, with the aim to ensure proper working, mounting and meshing of considered gears. Furthermore, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed in this work. However, the objective functions and developed constraints of the considered Multiobjective planetary gearbox optimization problem are nonlinear and multimodal functions, and therefore the global optimal solution cannot be obtained using the conventional optimization methods. Therefore, in order to solve this multiobjective and complex optimization problem, the research in this dissertation is focused on metaheuristic optimization algorithms, which belong to the group of evolutionary algorithms, including: differential evolution algorithm and genetic algorithm, as well as the algorithms inspired by the biological systems, such as particle swarm optimization algorithm. To overcome difficulties in solving complex optimization problems, in this thesis the considered algorithms are modified with the development of adaptive techniques for setting the values of control parameters and hybridization of algorithms. In order to create an effective hybrid algorithm, the hybridization process has been preceded by extensive numerical simulations and statistical analysis of advantages and disadvantages of each algorithm. Therefore, the proposed hybridization of algorithms and introduction of adaptive control parameters can successfully combine the advantages and avoid disadvantages of each algorithm. In this way, the proposed modifications successfully combine the advantages of each algorithm and avoid their disadvantages, thus significantly expanding the scale of implementation of the proposed algorithms for complex optimization problems..