scholarly journals IMPLEMENTASI DUA MODEL CROSSOVER PADA ALGORITMA GENETIKA UNTUK OPTIMASI PENGGUNAAN RUANG PERKULIAHAN

2021 ◽  
Vol 4 (2) ◽  
pp. 167-177
Author(s):  
I Wayan Supriana ◽  
Made Agung Raharja ◽  
I Made Satria Bimantara ◽  
Devan Bramantya
Keyword(s):  
Genetic Algorithm ◽  
Computation Time ◽  
Initial Population ◽  
Study Program ◽  
Fitness Value ◽  
Engineering Study ◽  
Mapping Process ◽  
Optimal Population ◽  
The Many ◽  
Optimal Space

The lecture mapping process is often hampered by the number and capacity of rooms, this condition often occurs because of the many obstacles that must be fulfilled. For example, there are courses offered in one semester that cannot be slots in space and time and the lecturer can teach at the same time for different courses. This is experienced by the Informatics Engineering Study Program of the Faculty of Mathematics and Natural Sciences, Udayana University, which offers a fairly large subject in each semester, causing optimization of the lecture space to often experience problems. The Genetic Algorithm (GA) is a model in the optimization of lecture space based on the natural selection mechanism through; coding problem, generate initial population, calculate fitness value, selection, crossover, mutation and optimal population. In this research, the optimization process implements two crossover models in the genetic algorithm, namely the n-point crossover and the cycle crossover. Based on the research that has been carried out, two crossover models provide optimal space usage mapping. From testing the n-point crossover model system gives the best fitness 1 in the 361 generation with a computation time of 11.08 while the cycle crossover model produces the best fitness 1 in the 361 generation with a computation time of 15.08.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

scholarly journals Optimasi Beban Mengajar Dosen Pendidikan Informatika di STKIP Bumi Persada Menggunakan Algoritme Genetika

2019 ◽  
Vol 1 ◽  
pp. 972
Author(s):  
Teuku Afriliansyah
Keyword(s):  
Genetic Algorithm ◽  
Fixed Number ◽  
Routine Activity ◽  
Test Results ◽  
Optimal Parameters ◽  
Study Program ◽  
Random Mutation ◽  
Fitness Value ◽  
Selection Test ◽  
The Cost

The cost of teaching lecturers is a routine activity conducted by all universities, especially the maintainers of departments in each faculty. This is done because the number of courses planned students are in every semester is always different and faced with a relatively fixed number of lecturers. Determining the teaching burden of lecturers must be done so that the teaching burden of lecturers does not exceed the maximum possible limit and the teaching process is done in accordance with the interest of lecturer study. Study Program of informatics Education High School and Educational Sciences Earth Persada Lhokseumawe still do the process of determining the teaching burden of the lecturer with the manual so that it takes a little time because it must adjust the infirmity Courses with a lecturer study interest. One of the methods of optimization that is able to solve the problem is genetic algorithm. The genetic algorithm process in this research includes representation with integer numbers, crossover methods with one cut point crossover, mutation methods with Reciprocalexchange mutation and random mutation, as well as selection methods with elitism Selection. Test results that have been tested show optimal parameters i.e. population size 60, combination of CR and Mr Value respectively 0.4, Sertta generation of 3576 with the largest fitness value produced is 0.082846.

scholarly journals OPTIMASI MULTI TRAVELLING SALESMAN PROBLEM (M-TSP) UNTUK DISTRIBUSI PRODUK PADA HOME INDUSTRI TEKSTIL DENGAN ALGORITMA GENETIKA

2017 ◽  
Vol 4 (2) ◽  
pp. 125
Author(s):  
Agung Mustika Rizki ◽  
Wayan Firdaus Mahmudy ◽  
Gusti Eka Yuliastuti
Keyword(s):  
Genetic Algorithm ◽  
Textile Industry ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Travelling Salesman ◽  
Fitness Value ◽  
Optimal Value ◽  
Distribution Process ◽  
Optimal Population ◽  
The Cost

<p><em>In the field of textile industry, the distribution process is an important factor that can affect the cost of production. For that we need optimization on the distribution process to be more efficient. This problem is a model in the Multi Trave</em><em>l</em><em>ling Salesman Problem (M-TSP). Much research has been done to complete the M-TSP model. Among several methods that have been applied by other researchers, genetic algorithms are a workable method for solving this model problem. In this article the authors chose the genetic algorithm is expected to produce an optimal value with an efficient time. Based on the results of testing and analysis, obtained the optimal population amount of 120. For the optimal generation amount is 800. The test results related to the number of population and the number of generations are used as input to test the combination of CR and MR, obtained the optimal combination of CR = 0 , 4 and MR = 0.6 with a fitness value of 2.9964.</em></p><p><em><strong>Keywords</strong></em><em>: Textile Industry, Multi Travelling Salesman Problem (M-TSP), Genetic Algorithm</em></p><p><em>Pada bidang industri tekstil, proses distribusi merupakan satu faktor penting yang dapat berpengaruh terhadap biaya produksi. Untuk itu diperlukan optimasi pada proses distribusi agar menjadi lebih efisien. Masalah seperti ini merupakam model dalam Multi Travelling Salesman Problem (M-TSP). Banyak penelitian telah dilakukan untuk menyelesaikan model M-TSP. Diantara beberapa metode yang telah diterapkan oleh peneiti lain, algoritma genetika adalah metode yang bisa diterapkan untuk penyelesaian permasalahan model ini. Dalam artikel ini penulis memilih algoritma genetika diharapkan dapat menghasilkan nilai yang optimal dengan waktu yang efisien. Berdasarkan hasil pengujian dan analisis, didapatkan jumlah populasi yang optimal sebesar 120. Untuk jumlah generasi yang optimal adalah sebesar 800. Hasil pengujian terkait jumlah populasi dan jumlah generasi tersebut dijadikan masukan untuk melakukan pengujian kombinasi  CR dan MR, didapatkan kombinasi yang optimal yakni CR=0,4 dan MR=0,6 dengan nilai fitness sebesar 2,9964.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>Industri Tekstil, Distribusi, Multi Travelling Salesman Problem (M-TSP), Algoritma Genetika</em></p>

scholarly journals Genetic Algorithm for lecturing schedule optimization

2019 ◽  
Vol 13 (1) ◽  
pp. 83 ◽  
Author(s):  
David Kristiadi ◽  
Rudy Hartanto
Keyword(s):  
Genetic Algorithm ◽  
Optimal Schedule ◽  
Optimal Solution ◽  
Computation Method ◽  
Scheduling Problems ◽  
Study Program ◽  
Directed Mutation ◽  
Classic Problem ◽  
Mutation Operators ◽  
Fitness Value

Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.

An Improved Genetic Algorithm on Hybrid Information Scheduling

2019 ◽  
Vol 13 (4) ◽  
pp. 416-423 ◽  
Author(s):  
Jingmei Li ◽  
Qiao Tian ◽  
Fangyuan Zheng ◽  
Weifei Wu
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Selection Strategy ◽  
Initial Population ◽  
List Scheduling ◽  
Solution Quality ◽  
Fitness Value ◽  
Information Scheduling

Background: Patents suggest that efficient hybrid information scheduling algorithm is critical to achieve high performance for heterogeneous multi-core processors. Because the commonly used list scheduling algorithm obtains the approximate optimal solution, and the genetic algorithm is easy to converge to the local optimal solution and the convergence rate is slow. Methods: To solve the above two problems, the thesis proposes a hybrid algorithm integrating list scheduling and genetic algorithm. Firstly, in the task priority calculation phase of the list scheduling algorithm, the total cost of the current task node to the exit node and the differences of its execution cost on different processor cores are taken into account when constructing the task scheduling list, then the task insertion method is used in the task allocation phase, thus obtaining a better scheduling sequence. Secondly, the pre-acquired scheduling sequence is added to the initial population of the genetic algorithm, and then a dynamic selection strategy based on fitness value is adopted in the phase of evolution. Finally, the cross and mutation probability in the genetic algorithm is improved to avoid premature phenomenon. Results: With a series of simulation experiments, the proposed algorithm is proved to have a faster convergence rate and a higher optimal solution quality. Conclusion: The experimental results show that the ICLGA has the highest quality of the optimal solution than CPOP and GA, and the convergence rate of ICLGA is faster than that of GA.

Distribution Network Reactive Power Planning Based on Improved Evolutionary Strategy Theory

2013 ◽  
Vol 732-733 ◽  
pp. 1023-1028
Author(s):  
Si Qing Sheng ◽  
Xing Li ◽  
Yang Lu
Keyword(s):  
Reactive Power ◽  
Distribution Network ◽  
Load Condition ◽  
Optimal Solution ◽  
Evolution Strategy ◽  
Evolutionary Strategy ◽  
Initial Population ◽  
Fitness Value ◽  
Cauchy Mutation ◽  
Reactive Power Planning

In this paper a distribution network reactive power planning mathematical model was established, taking the minimized sum of electrical energy loss at the different load operation modes and the investment for reactive power compensation equipments as objective function to solve the planning question respectively and taking the transformer tap as equality constraint. The evolution strategy is improved, The Euclidean distance is introduced into the formation of the initial population, and the initial population under the max load operation mode is based on the optimal solution of the min load condition. The Cauchy mutation and variation coefficient are introduced into the evolution strategy method. By means of improvement of fitness to ensure diversity of population in early and accuracy of the fitness value.

scholarly journals A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chenghua Shi ◽  
Tonglei Li ◽  
Yu Bai ◽  
Fei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Split Delivery ◽  
Soft Time Windows ◽  
Comparison Results ◽  
Potential Demand ◽  
The Cost

We present the vehicle routing problem with potential demands and time windows (VRP-PDTW), which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA) for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA) and heuristics-based genetic algorithm (HGA) from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

2018 ◽  
Vol 7 (4.33) ◽  
pp. 130
Author(s):  
Atiqa Zukreena Zakuan ◽  
Shuzlina Abdul-Rahman ◽  
Hamidah Jantan ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Succession Planning ◽  
Fitness Function ◽  
Academic Staff ◽  
Fitness Value ◽  
Selection Decision ◽  
The University ◽  
Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.    

Genetic Algorithm-Based Searching Method for Piping Path Routing in Coal-Fired Boiler Buildings

2011 ◽  
Author(s):  
Naohiro Kusumi ◽  
David E. Goldberg ◽  
Noriyuki Ichinose
Keyword(s):  
Genetic Algorithm ◽  
Computer Aided Design ◽  
Steel Structures ◽  
Search Space ◽  
Design System ◽  
Dynamic Programming Method ◽  
Plant Design ◽  
Technical Specifications ◽  
The Many ◽  
Searching Method

Power plant design using digital engineering based on 3-D computer-aided design has become a mainstream technology because of possessing higher speed and improvement in design accuracy. To take a coal-fired boiler building as an example, it has many complex structures with several million parts including the boiler itself, large fans, steel structures, and piping in varying sizes. Therefore, it is not easy to maintain integrity of the whole design throughout all the many engineering processes. We have developed a smart design system for coal-fired boiler buildings to solve the integrity problem. This system is capable of creating and allocating 3-D models automatically in accordance with various technical specifications and engineering rules. Lately, however, there has been a growing demand for more effectiveness of the developed system. We have begun to look into the feasibility of further improvements of the system function. The first point to note, when considering effectiveness, is the piping path routing process in the coal-fired boiler building. The quantity of piping is large, and it has a considerable impact on performance of the whole plant because hot steam is fed into the steam turbine and cold steam is taken from it through the piping. A considerable number of studies have been made on automatic searching methods of piping path routing. Although, the decision of piping path routing by using the Dynamic Programming method is most commonly, a previously decided routing becomes an interference object because of the single searching method. Therefore, basically, the later the order of the routing becomes, the longer the length of the routing becomes. To overcome this problem, in this paper we have proposed a new searching method based on the Genetic Algorithm (GA). The GA is a multipoint searching algorithm based on the mechanics of natural selection and natural genetics. Virtual prohibited cells are introduced into the proposed search method as a new idea. The virtual prohibited cells are located in a search space. The different paths are generated by avoiding the virtual prohibited cells while searching for the piping path routing. The optimum locations of the prohibited cells which are expressed in a genotype are obtained by using the GA in order to get a lot of paths independent of the order of the routing. The proposed method was evaluated using a simple searching problem. The results showed that many effective paths are generated by making the virtual prohibited cells.

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