OPTIMIZATION OF TEST KEEPER SCHEDULING USING GENETIC ALGORITHM AT INFORMATICS DEPARTMENT PETRA CHRISTIAN UNIVERSITY

before mid or final exam, there will be a manual process to schedule the test keepers for every exam session. The test keepers are lecturer assistants (assistant is an appointed student to help lecturer in class). For an exam session, the keeper can be 1 up to 3 people, depending on the exams participant. These manual process is considering many factors, i.e. the assistants batch (year), the average of exams participant batch(year), gender combination of the keeper, evenness of the exam keeping of every assistant, the character of the assistant itself, and the exam schedule of the assistant. These factors are considered upon picking every exam sessions keeper, which is taking a lot of time and knowledge, and this process is done twice a semester by an exam coordinator (lecturer). In this paper, will be designed an application that is using genetic algorithm to automatically assign the test keepers for every exam. The result of the application is tested during the mid-exam and final-exam early semester of 2016, and the application is giving a good result, with the accuracy of 90.23%, in which the 9.77% is some minor changes that is required to make the test keepers more suitable

Population based optimization algorithms improvement using the predictive particles

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate

Optimisation of a Nacelle Electro-Thermal Ice Protection System for Icing Wind Tunnel Testing

Abstract Aircraft are equipped with ice protection systems (IPS), to avoid, delay or remove ice accretion. Two widely used technologies are the thermo-pneumatic IPS and the electro-thermal IPS (ETIPS). Thermo-pneumatic IPS requires air extraction from the engine negatively affecting its performances. Moreover, in the context of green aviation, aircraft manufacturers are moving towards hybrid or fully electric aircraft requiring all electric on-board systems. In this work, an ETIPS has been designed and optimised to replace the nacelle pneumatic-thermal system. The aim is to minimise the power consumption while assuring limited or null ice formation and that the surface temperature remains between acceptable bounds to avoid material degradation. The design parameters were the length and heat flux of each heater. Runback ice formations and surface temperature were assessed by means of the in-house developed PoliMIce framework. The optimisation was performed using a genetic algorithm, and the constraints were handled through a linear penalty method. The optimal configuration required 33% less power with respect to the previously installed thermo-pneumatic IPS. Furthermore, engine performance is not affected in the case of the ETIPS. This energy saving resulted in an estimated reduction of specific fuel consumption of 3%, when operating the IPS in anti-icing mode

Study of multi-objective optimization and its implementation using NSGA-II

This project investigates the Multi-objective optimization strategies and their solutions using Multi-objective evolutionary algorithms (MOEAs). Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing are criticized mainly for their; a) computational complexity, b) lack of elitism, c) need for specifying sharing parameter. In this paper the Non- Dominated Sorting Genetic Algorithm (NSGA) is studied and NSGA-II as proposed by Deb et. al. has been implemented, which alleviates the above three difficulties. In this study different objectives have been considered with different variables and constraints. The algorithm yielded satisfactory simulation results in all the different cases. The effect of the genetic parameters on the Pareto-Optimal front in all the cases has been studied. The results show that NSGA-II find much better spread of solutions and better convergence near the true pareto optimal front compared to other elitist MOEAs

Application of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experiments

YesThe work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient Design of Experiment (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to support an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of Model Building–Model Validation (MB-MV) sequences, by generating optimal infill sets of test points as OLH DoEs, that preserve good space filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB-MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back (SHCB) function, as well as the Gasoline Direct Injection (GDI) engine steady state engine mapping problem that motivated this research. The case studies show that the algorithm is effective at delivering quasi-orthogonal space-filling DoEs with good properties even after several MB-MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, also is that significant reduction in the effort and cost of testing can be achieved.The research work presented in this paper was funded by the UK Technology Strategy Board (TSB) through the Carbon Reduction through Engine Optimization (CREO) project

Specific Parameter-Free Global Optimization to Speed Up Setting and Avoid Factors Interactions

Meta-heuristics utilizing numerous parameters are more complicated than meta-heuristics with a couple of parameters for various reasons. In essence, the effort expected to tune the strategy-particular parameters is far more prominent as the quantity of parameters increases and furthermore, complex algorithms are liable for the presence of further parameter interactions. Jaya meta-heuristic does not involve any strategy-specific parameters and is a one-stage technique. It has demonstrated its effectiveness compared to major types of meta-heuristics and it introduces various points of interest, such as its easy deployment and set-up in industrial applications and its low complexity to be studied. In this work, a new meta-heuristic, Enhanced Jaya (EJaya) is proposed to overcome the inconsistency of Jaya in diverse situations, introducing coherent attraction and repulsion movements and restrained intensity for flight. Comparative results of EJaya in a set of benchmark problems including statistical tests show that it is feasible to increase the accuracy, scalability and exploitation capability of Jaya while keeping its specific parameter-free feature. EJaya is especially suitable for a priori undefined characteristics optimization functions or applications where the set-up time of the optimization process is critical and parameters tuning and interactions must be avoided

Cislunar Trajectory Generation with Sun-Exclusion Zone Constraints Using a Genetic Algorithm and Direct Method Hybridization

Space missions to the Moon have received renewed interest in recent decades. Science missions continue to be sent to the Moon, and several space agencies have aspirations of establishing a human presence on the Moon. With the increased number of artificial objects in cislunar space, the problem of tracking these objects arises. Optical sensors are able to track these objects in deep space. However, optical sensors cannot track objects that are close to the Sun as viewed from the observer. This unobservable region is the Sun-exclusion zone (SEZ). This research attempts to create optimal Moon-Earth transfers which are completely in the SEZ using a genetic algorithm-direct method hybridization. Such transfers demonstrate how much the SEZ can limit optical sensors from maintaining custody of a satellite. Transfers from L1 and L2 Lyapunov orbits to geosynchronous orbit are generated while optimizing fuel and time of flight. Remaining inside of the SEZ is shown to significantly increase the fuel required to make the transfer

Aperiodic Multilayer Graphene Based Tunable and Switchable Thermal Emitter at Mid-infrared Frequencies

Over the past few decades, there have been tremendous innovations in electronics and photonics. The development of these ultra-fast growing technologies mostly relies on fundamental understanding of novel materials with unique properties as well as new designs of device architectures with more diverse and better functionalities. In this regard, the promising approach for next-generation nanoscale electronics and photonics is to exploit the extraordinary characteristics of novel nanomaterials. There has been an explosion of interest in graphene for photonic applications as it provides a degree of freedom to manipulate electromagnetic waves. In this thesis, to tailor the broadband blackbody radiation, new aperiodic multilayer structures composed of multiple layers of graphene and hexagonal boron nitride (hBN) are proposed as selective, tunable and switchable thermal emitters. To obtain the layer thicknesses of these aperiodic multilayer structures for maximum emittance/absorptance, a hybrid optimization algorithm coupled to a transfer matrix code is employed. The device simulation indicates that perfect absorption efficiency of unity can be achieved at very narrow frequency bands in the infrared under normal incidence. It has been shown that the chemical potential in graphene enables a promising way to design electrically controllable absorption/emission, resulting in selective, tunable and switchable thermal emitters at infrared frequencies. By simulating different aperiodic thermal emitters with different numbers of graphene layers, the effect of the number of graphene layers on selectivity, tunability, and switchability of thermal emittance is investigated. This study may contribute towards the realization of wavelength selective detectors with switchable intensity for sensing applications