Genetic Algorithm Method in Examination Timetabling Problem: A Survey

Numerical analysis of circular straight mufflers equipped with three chambers at high-order-modes

Parameter identification is the process of constructing the mathematical model of a dynamic system based on measured dynamic responses. A non-classical search method namely genetic algorithm (GA) is employed in this study, which has several advantages over classical system identification techniques. Nevertheless, direct application of GA does not necessarily work well, particularly with regards to computational efficiency in fine-tuning when the solution approaches the optimal value. Thus, a local search (LS) method is introduced into the GA approach to improve this capability. Numerical example of a cantilever beam is presented to show the efficiency of the combined GA and LS method (GA-LS). The FE model of the cantilever beam is established with 10 beam elements. In this example, only the element properties associated with flapping motion are identified. The GA-LS method gives much better results than the GA method does. The mean error is reduced to 5.3% from 14.9% while to 14.5% from 47.2% for the maximum individual error. It is concluded that the GA-LS method is a more efficient search method than the GA method. The GA-LS method is then applied to identify the mass and stiffness parameters of a model helicopter blade using measured excitations and accelerations in this paper. Since the mass and stiffness of the model helicopter blade are not available, the only way to verify the identified parameters is to compare the ffequencies and mode shapes calculated from the identified parameters and the measured frequencies and modal shapes of the actual blade. The mean eiror of the frequencies is 0.9% and the maximum individual error 2.4%. The sequence of identified mode shapes is the same with measured mode shapes. Transactions on Engineering Sciences vol 43, © 2003 WIT Press, www.witpress.com, ISSN 1743-3533

An informed genetic algorithm for the examination timetabling problem

The thickness of an aquifer (B) is a critical hydrogeologic parameter in understanding groundwater system, especially in a karst aquifer where the high heterogeneity in this parameter brings more uncertainty to the evaluation of groundwater resources. In this study, the genetic algorithm (GA) method was used to estimate B, as well as five other parameters, including K r (horizontal hydraulic conductivity), K z (vertical hydraulic conductivity), S (storativity), S y (specific yield), and α (delayed coefficient) of a karst aquifer based on a pumping test. The search bounds of the six parameters for the GA method were obtained from numerous hypothetic numerical tests. The feasibility of the GA method for parameter estimation was verified by the results obtained from the gradient method in terms of a pumping test conducted in the Platte River valley in Nebraska. Then, this approach was used to estimate the hydraulic parameters in a karst aquifer with unknown aquifer thickness based on a pumping recovery test conducted in the Houzhai karst basin, southwestern China. The results show that the K r , K z , and the anisotropy ratio of K r to K z are strongly affected by B. The differences between the estimated parameter values at two observation wells indicate the existence of high heterogeneity in the karst aquifer. The parameter B has the highest absolute normalized sensitivity among these variables, implying that the estimated B may be more reliable as compared to other parameters. This study demonstrates that the GA method is an alternative method of parameter estimation with the unknown aquifer thickness.


 
 
 
 Abstract:
 Aim: This study is on tackling Examination Timetabling Problem (ETP) of the Faculty of Economics And Administrative Sciences (FEAS) of the Ankara HBV University summer school, where the courses of fall and spring semesters are offered simultaneously and regulations on restricting enrollments in inter-department electives or in-department courses of distinct years are relaxed. Thus, the complexity of the nature of the ETP problem is exacerbated. The direct heuristics based on successive assignments that the university normally adopts was proven inadequate for assuming standard regulations hence, another approach we explain in this paper was needed.
 Design / Research methods: The ETP was formulated as a Linear Mixed-Integer Program (LMIP) and decomposed into three stages; timetabling exams, room assignment, student allocation. To manage the conflict between the stakeholders of the examination procedure, a lexicographic optimization process based on the priority of the parties was undertaken.
 Conclusions / findings: After a recursive timetabling process based on a trial-and-error method a clash-free timetable was generated and, a room assignment plan that minimizes the total number of proctoring duties, usage of higher floor rooms and total crowdedness of rooms respectively was put into action. Therefore no student group experienced any clashing exams, the faculty members saved time that can be spent on research instead, since the room usage was better planned the costs (elevator usage, lighting, air conditioning, the labor of the janitors) were assumed to be decreased.
 Originality / value of the article: Each examination period bares a different ETP due to its problem-specific nature (number of courses offered, structure of student enrollments, availability of rooms, etc.). Summer schools provide a more irregular structure that demands special attention, a trial-and-error reformulation of the ETP in our case. In addition, the traditional formulations of the ETP, to the extent we have been able to scan, do not include the minimization of the crowdedness of the rooms. Thus, in creating a more comfortable environment, easier to monitor exams and, ability in handling unexpected dysfunctionalities (broken classroom equipment, etc.) this study is novel.
 Limitations of the research: The algorithms to solve an ETP formulated as an LMIP are of high complexity therefore, we are not able to assert the optimality of our suggested solutions acquired within time limitations.
 
 
 
 
 Keywords: examination timetabling, group decision making, lexicographic optimization, linear mixed-integer programming
 JEL: C44, C61, M12
 
 
 

University examination timetabling problem is a complicated, multi-constraint combinatorial optimization problem. The current grouping genetic algorithm could get a better optimistic solution. Although the block encoding technique was applied in, the probabilities of crossover and mutation operation were still based on simple genetic algorithm. So the universality of this algorithm is not very well because the same probabilities of crossover and mutation operation could obtain different effects in different cases of examination arrangements. To find a more general algorithm, an Improved Adaptive Genetic Algorithm (IAGA) for practical applications was presented in this paper. IAGA can reconstruct the probabilities of crossover and mutation operation according to different cases of examination arrangements. To make further improvement of its convergence speed and overcome premature problem, IAGA appropriately adjusts mutation operation by Population's Maturity in addition. Moreover, the Memory Operator was added into the Algorithm to ensure getting the global optimization solution. Finally, IAGA was tested in function optimization and Examination Timetabling problems, and the experiments show that the results are promoted very well.

IntroductionIn recent years, SMPMSM is quietly famous compared with other conventional motors in industrial applications due to higher efficiency, higher reliability, low cost, and smooth torque [1]–[3]. In general, several objective optimizations need to be consider, which required an optimization technique for an electrical motor. This paper deals with an optimal design of a surface-mounted permanent magnet synchronous machine (SMPMSM) with an exact analytical subdomain model by using a genetic algorithm method. To analyze the characteristic of permanent magnet (PM) motors, the classical optimization method, such as the finite element method (FEM), is intensively used. However, FEM has several time problems that require a longer computational time to evaluate the performance of PM motors. This problem can be overcome by using a genetic algorithm (GA) method combined with a subdomain model (SD), which developed an improved performance of SMPMSM, for instance, total harmonic distortion (THDv) and phase back-EMF. In this design, a three-phase 12-slot/8-pole PM motor is established with an exact SD model with radial magnetization patterns. Then, the GA ensemble with SD model to search the optimality of SMPMSM machine design. In the final analysis, the optimal new design of SMPMSM demonstrated by comparing with the initial design that is investigated by FEM. The result of induced back-EMF, and magnetic flux density of optimal design is compared with the initial design to show the advantages of GA optimization method.Analytical Model, Genetic Algorithm and Parameter of optimizationThe purpose of this analysis is to investigate the optimization design of a three-phase 12-slot/8-pole SMPMSM with a radial magnetization pattern. This optimization problem has been solved by obtains the minimum THDv as a fitness function. In order to process the optimization design of SMPMSM, several parameters need to be optimized i.e magnet pole-pitch, αp and slot-opening angle, boa. The roulette method is used to select the objective function of optimization with 50 maximum generations per iteration. The cross and mutation probability need to be set which requires linear and uniform recombination which is 0.89 and 0.025 respectively. The parameters of 12-slot/8-pole SMPM motor as follows: radius of the rotor, Rr = 23mm, the radius of PM surface Rm = 26mm, the inner radius of the stator Rsb = 42.5mm, axial length, la = 50mm, air-gap length, lg = 1mm, remanence of the magnet, Br, 1.12T, relative coil permeability, µr = 1.05 with the number of pole pairs, Np is 8 and number of stator slots, Ns is 12.The result, Discussion and ConclusionThe phase back electromotive force has a significant relationship with flux-linkage because phase back electromotive force is derived from the rate of change of flux-linkage. In Fig. 1. show the waveform of phase back electromotive force against rotor position of initial and optimal design in radial magnetization patterns. The initial design of phase back electromotive force waveform shows more trapezoidal compare with the optimal design which has a sinusoidal waveform. The comparison of total harmonic distortion between the initial and optimal design of SMPMSM are demonstrated in Fig. 2 below. The result from Fig.2. shows the highest total harmonic distortion of 12-slot/8-pole motor is an initial design which is 11.34% compared with the optimal design where 1.3%. The magnitude of harmonic in phase back electromotive force |En| of initial design shows 22.24 V compare to optimal design which is 17.4 V. This shows a good agreement that the optimal motor design gives lowest total harmonic distortion compare with the initial design. The optimizes of SMPMSM motor with the following parameters are given in Table 1. **

A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

In an optical Pancharatnam-Berry (PB) phase metasurface, each sub-wavelength dielectric structure of varied spatial orientation can be treated as a point source with the same amplitude yet varied relative phase. In this work, we introduce an optimized genetic algorithm (GA) method for the synthesis of one-dimensional (1D) PB phase-controlled dielectric metasurfaces by seeking for optimized phase profile solutions, which differs from previously reported amplitude-controlled GA method only applicable to generate transverse optical modes with plasmonic metasurfaces. The GA–optimized phase profiles can be readily used to construct dielectric metasurfaces with improved functionalities. The loop of phase-controlled GA consists of initialization, random mutation, screened evolution, and duplication. Here random mutation is realized by changing the phase of each unit cell, and this process should be efficient to obtain enough mutations to drive the whole GA process under supervision of appropriate mutation boundary. A well-chosen fitness function ensures the right direction of screened evolution, and the duplication process guarantees an equilibrated number of generated light patterns. Importantly, we optimize the GA loop by introducing a multi-step hierarchical mutation process to break local optimum limits. We demonstrate the validity of our optimized GA method by generating longitudinal optical modes (i. e., non-diffractive light sheets) with 1D PB phase dielectric metasurfaces having non-analytical counter-intuitive phase profiles. The produced large-area, long-distance light sheets could be used for realizing high-speed, low-noise light-sheet microscopy. Additionally, a simplified 3D light pattern generated by a 2D PB phase metasurface further reveals the potential of our optimized GA method for manipulating truly 3D light fields.

In this study, a high-power synchronous reluctance motor (SynRM) was designed for the traction motor of electric vehicle (EV) and its double-stage optimization was performed. Genetic algorithm and sensitivity analysis methods were used to obtain the best design parameters. Double-stage optimization was carried out to minimize the torque ripple and obtain the targeted torque, speed, and power values of the SynRM. In the first stage, the genetic algorithm method was used to improve the design parameters of the stator and rotor. With the improved design parameters, it was observed that the torque ripple decreased. In the second stage, the sensitivity analysis method was used. In this method, the effect of changing the skew angle of the stator on the torque ripple was investigated. The performance of the designed motor was examined in the optimization process. It was observed that the targeted torque, power, speed, efficiency, and torque ripple minimization values are successfully achieved with the best stator and rotor parameters. The results showed that SynRM produces high torque and high power with high efficiency and low torque ripple over wide speed range. It is quite proper to use the designed SynRM as a traction motor of new generation electric vehicles.

Due to the rapid growth of the retail shop trend recently, the Distribution Center (DC) or warehouse had become to be a vital part in the supply chain in order to store goods from multiple suppliers and deliver to multiple retailers. Cross docking is a relatively the logistics technique used in the DC in order to move materials from inbound trailers to outbound trailers as quickly as possible. From the following problem, an ineffectual DC management leads to the resources utilization problem such as machine, forklift, industrial trucks, etc. The main objective of this research is to study and solve the scheduling problem of the usage of the forklift in the Cross docking of DC and to manage the inbound and outbound docks in order to minimize Tardiness jobs, and also to maximize the efficiency of machine utilization. This study has provided the scheduling, comparison from genetic algorithm and heuristic method. As the result came out we found that the forklift scheduling by the genetic algorithm method is given the better management result better than heuristic method.

Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, whereas each regime following an autoregressive (AR) model. The division of the regime based on the score of the delay and threshold of the data itself. The number of SETAR model parameters not only resulted from the best model search process but also resulted in a SETAR model that is not yet optimum. Based on these findings, this study used Genetic Algorithm (GA) to produce the best and optimum SETAR model. In this research, using SETAR simulation data modeling and return data of Bank Rakyat Indonesia (BRI) were performed. The method used to model the data is Grid Search (GS) and Genetic Algorithm (GA). The result of analysis of SETAR simulation data shows that GA method gives better modeling result than GS method. The GA motive AIC value for the amount of 200 data is -3.976178 which is smaller than the AIC GS method of 1.361723. For the amount of data of 500 AIC values, GA method is also smaller than AIC GS method. In BRI stock return data, GA method also gives better modeling result compared to GS. It is marked by the GA AIC method value of -11147.66 less than -11146.26 which is the AIC method of GS. Thus, the result of analysis of SETAR model simulation data and BRI stock return shows that GA method gives better modeling result compared to GS method based on generated AIC value.

In this study, a new model was developed to estimate solar radiation using the genetic algorithm method in the MATLAB program. When the literature was reviewed, it is observed that there are a lot of different methods used to estimate solar radiation. In the literature review, it was seen that the genetic algorithm approach was not used in solar radiation estimation before. In this study, the coefficients of the Angstrom equation were calculated through a genetic algorithm by using the 5‐year solar radiation data of Adana province, and accordingly, a new model was developed. The suggested model was compared to various models published in the literature for determining the solar radiation density of the given region. It was evaluated that the new developed model using a genetic algorithm has acceptable performance for estimating solar radiation.

The examination timetabling problem involves assigning exams to a specific or limited number of timeslots and rooms with the aim of satisfying all hard constraints (without compromise) and satisfying the soft constraints as far as possible. Most of the techniques reported in the literature have been applied to simplified examination benchmark data sets. In this paper, we bridge the gap between research and practice by investigating a problem taken from the real world. This paper introduces a modified and extended great deluge algorithm (GDA) for the examination timetabling problem that uses a single, easy to understand parameter. We investigate different initial solutions, which are used as a starting point for the GDA, as well as altering the number of iterations. In addition, we carry out statistical analyses to compare the results when using these different parameters. The proposed methodology is able to produce good quality solutions when compared with the solution currently produced by the host organisation, generated in our previous work and from the original GDA.

This paper proposed particle swarm optimizations of proportional-integral-derivative (PID) gains used in the disturbance observer based control of Proton Exchange Membrane Fuel Cell (PEMFC) generation system. The proposed approach is easy to implement and have good computational efficiency. In order to get satisfactory dynamic performance, a new performance index is introduced. Comparisons between the results obtained by Genetic Algorithm (GA) method and those by Particle Swarm Optimizations (PSO) method are made. Numerical results show that the gains obtained by PSO are more optimal than the GA method, and Particle swarm optimizations take the least time to achieve the same optimal gains.