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Journal articles on the topic 'Genetic Algorthim'
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Yani, Ahmad, Junaidi Junaidi, M. Irwanto, and A. H. Haziah. "Optimum reactive power to improve power factor in industry using genetic algortihm." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 2 (May 1, 2019): 751. http://dx.doi.org/10.11591/ijeecs.v14.i2.pp751-757.
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<span>Capacitor bank is a collection of power tools in the form of a capacitor that serves as a tool that can reduce or improve reactive power into the power grid. The load on the electricity network in general is an inductive load. If the average power factor (cos ϴ) is less than 0.85, the State Electricity Company will provide the reactive power in KVAR fines usage charges on customers. An effort should be done to reduce the reactive power. An installation of bank capacitor is suitable to be implemented in an industry AC loads. It will reduce the reactive power and improve the power factor. In the case of 380 V, 50 Hz, 500 kW AC loads are improved the power factor from 0.7 to 0.93 using genetic algorithm, thus the AC loads current and reactive power will be decreased. It is suitable that the AC loads current is inversely proportional to the power factor, and the reactive power is proportional to the AC loads current.</span>
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Nurdin, Hardisal, Muhammad Zarlis, and Erna Budhiarti Nababan. "Teknik Watermarking Adaptif Menggunakan Micro Genetic Algorithm." Jurnal Inotera 1, no. 1 (July 27, 2017): 64. http://dx.doi.org/10.31572/inotera.vol1.iss1.2016.id9.
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Citra digital sangat rentan terhadap perlakuan pengolahan kembali, seperti pemampatan, penyaringan, pengaburan, dan sebagainya. Pada citra yang mengandung watermark, pengolahan kembali tentunya dapat merusak eksistensi watermark di dalamnya. Kekukuhan watermark dalam citra asli dapat ditingkatkan melalui pemilihan teknik-teknik penyisipan yang tepat. Salah satunya dengan menggunakan teknik transformasi citra seperti discrete wavelete transform atau DWT. Namun penentuan skala penyisipan (gain) bagi watermark menjadi sangat krusial, karena dengan nilai gain yang besar akan membuat kualitas visual citra dapat berkurang. Sebaliknya, nilai gain yang terlalu kecil membuat watermark akan sukar untuk dideteksi. Penggunaan teknik cerdas seperti micro genetic algorthm dapat memberika solusi dalam menentukan nilai penskalaan ini. Sehingga kualitas visual citra dapat dijaga dan watermark di dalamnya dapat dipertahankan.
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Alsuwaiket, M. A. "Feature Extraction of EEG Signals for Seizure Detection Using Machine Learning Algorthims." Engineering, Technology & Applied Science Research 12, no. 5 (October 2, 2022): 9247–51. http://dx.doi.org/10.48084/etasr.5208.
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Epilepsy is a central nervous system disorder in which brain activity becomes abnormal and causes periods of unusual behavior and sometimes loss of awareness. Epilepsy is a disease that may affect males or females of all ethnic groups and ages. Detecting seizures is challenging due to the difference in human behaviors and brain signals. This paper aims to automate the extraction of electroencephalogram (EEG) signals without referring to doctors using two feature extraction methods, namely Wavelet Packet decomposition (WPD) and Genetic Algorithm-Based Frequency-Domain Feature Search (GAFDS). Three machine learning algorithms were applied, namely Conventional Neural Networks (CNNs), Support Vector Machine (SVM), and Random Forest (RF) to diagnose epileptic seizures. The results achieved from the classifiers show a higher accuracy rate using CNNs as a classifier and GAFDS as feature extraction reaching 97.93% accuracy while the accuracy rate of the SVM and RF was 94.49% and 88.03% respectively.
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Nugroho, Herminarto, Muhammad Akram Saputra, and Muhammad Fadil Anwar. "Optimasi Daya Generator Angin Melalui Pitch Angle Control dengan Particle Swarm Optimization dan Genetic Algortihm." PETIR 16, no. 1 (April 25, 2023): 100–108. http://dx.doi.org/10.33322/petir.v16i1.1704.
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In optimizing the power of the wind turbine generator (WTG) due to fluctuating wind speed, the pitch angle control is used on WTG. The pitch angle has a great influence towards the rotational speed change of the WTG. The pitch angle value is in the range of 0-90 degrees. The optimal value of the pitch angle can produce the maximum power output from the wind turbine generator. Because the pitch angle value changes with wind speed, the optimization method is carried out using Particle Swarm Optimization (PSO) method and Genetic Algorithm (GA) optimization method. By using these two methods optimally, the optimal pitch angle will be obtained dependant on changes in wind speed.
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Sari, Sri Novida, Roberto Kaban, Abdul Khaliq, and Ayu Andari. "SISTEM PENJADWALAN MATA PELAJARAN SEKOLAH MENGGUNAKAN METODE HYBRID ARTIFICIAL BEE COLONY (HABC)." Jurnal Nasional Teknologi Komputer 2, no. 1 (February 1, 2022): 20–32. http://dx.doi.org/10.61306/jnastek.v2i1.21.
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scheduling is very closely related to teaching and learning activities in a school that aims to support, facilitate, enhance the quality of education and improve discipline in the process of teaching and learning activities. The problem faced in making a schedule of subjects is time, the process of making a schedule takes quite a long time in the process of processing the schedule. Of the algorithms used in scheduling problems, algortima Artificial Bee Colony is an algorithm inspired by the behavior of honeybee colonies that work based on the way bees forage for food. Algortima Artificial Bee Colony is known to have advantages over other optimization algorithms that are very efficient in finding optimal solutions. But the Artificial Bee Colony algorithm has its drawbacks that, if the dimensions of the problem increase, the exchange of information is still limited to one dimension. The weakness of Artificial Bee Colony is what makes the opportunity to develop ABC, namely, Hybrid Artificial Bee Colony (HABC) by adding a crossover operator of genetic algorithms. Genetics algorithm's crossover operator inserted into ABC algorithm to improve exchange of information between bees. It can be concluded that the application of HABC methods can do the optimization process quite well in scheduling issues with fairly low schedule clashing results. Based on the results of trials conducted five times, the results that scheduling applications using the HABC Algorithm can produce a schedule of subjects by clashing to a minimum with an average accuracy percentage rate of 98.03%
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Utami, Dwi Yuni, Elah Nurlelah, and Noer Hikmah. "Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 4, no. 1 (July 20, 2020): 76–85. http://dx.doi.org/10.31289/jite.v4i1.3793.
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Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.
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Li, Mouwei. "Mended genetic algorthms and application to profile parameters optimization of tandem cold strip mill." Chinese Journal of Mechanical Engineering (English Edition) 13, supp (2000): 123. http://dx.doi.org/10.3901/cjme.2000.supp.123.
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Yanto, Eri, and Ramalia Noratama Putri. "APPLICATION OF GENETIC ALGORITHM IN TOURISM ROUTE OPTIMIZATION IN PEKANBARU CITY." Journal of Applied Business and Technology 1, no. 1 (January 20, 2020): 41–50. http://dx.doi.org/10.35145/jabt.v1i1.22.
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The number of tourist attractions that are not yet well known, reinforced by the release of Pekanbaru City Government data that the tourism sector only accounts for about 0.9% of the national tourism sector. Therefore, this study aims to optimize the determination of Pekanbaru city tourist travel routes by using genetic algorithms or Genetic Algorithms. Genetic algortima process generally consists of several stages, starting from the initial generation, determination of fitness, crossover stage, mutation to the generation of advanced stages. With an accuracy rate of the best offered solutions reaching around 88% and an average solution search of about 19 seconds per iteration on a constant 100x trial, the results of this study can be used to help general users or Tour & Travel businesses in determining travel routes more optimal travel and a better travel experience.
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Li, Li. "Research on daylighting optimization of building space layout based on parametric design." Sustainable Buildings 7 (2024): 3. http://dx.doi.org/10.1051/sbuild/2024003.
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Excellent daylighting in buildings is beneficial to protect the physical and mental health of users. After introducing the daylighting of the building, this paper used the genetic algorithm (GA) optimized by co-evolution to optimize the daylighting. Then, a one-story L-shaped accommodation house in Zhengzhou, Henan Province was taken as a case for analysis. The effectiveness of the Daysim software used for calculating the building lighting indicator was tested. Then, the performance of the improved GA with different daylighting indicators as fitness values was compared. Finally, the optimization performance of the particle swarm optimization (PSO) algorithm, the traditional GA, and the improved GA were compared. The results showed that the daylighting indicators simulated by Daysim were significantly correlated with the measured data, suggesting its effectiveness. The improved GA using dynamic daylighting indicators as fitness values had better optimization performance. Compared with the other two algortihms, the improved GA had better optimization performance.
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Rohman, Ramdhan Saepul, Rizal Amegia Saputra, and Dasya Arif Firmansaha. "Komparasi Algoritma C4.5 Berbasis PSO Dan GA Untuk Diagnosa Penyakit Stroke." CESS (Journal of Computer Engineering, System and Science) 5, no. 1 (January 31, 2020): 155. http://dx.doi.org/10.24114/cess.v5i1.15225.
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Abstrak— Stroke merupakan gangguan fungsi otak baik lokal maupun menyeluruh yang disebabkan karena pasokan darah ke otak terganggu yang terjadi secara cepat dan berlangsung lebih dari 24 jam atau berakhir dengan kematian. Stroke Merupakan 1 dari 10 jenis penyakit yang paling mematikan di Indonesia. Hal ini berdasarkan pada data yang dikumpulkan dari sampel yang mewakili Indonesia, meliputi 41.590 kematian sepanjang 2014 dan pada semua kematian itu dilakukan autopsi verbal, sesuai pedoman Badan Kesehatan Dunia. Pentingnya mengetahui gejala sejak dini merupakan langkah awal dalam mencegahan terjadinya stroke. Maka itu, dilakukan penelitian untuk menganalisa data terkait dengan penyebab stroke. Adapun atribut yang terlibat dalam penyebab terjadinya stroke yakni, usia, jenis kelamin, kadar glukosa, riwayat penyakit jantung, hipertensi, tipe pekerjaan, tipe tempat tinggal, status merokok, index masa tubuh dan status pernikahan. Diperlukan suatu algortima tertentu untuk mengklasifikasikan semua atribut tersebut. C45 merupakan Algoritma yang paling banyak digunakan, dalam kasus ini akurasi dari algoritma C4.5 sebesar 99.07%. Selanjutnya Algoritma C4.5 dioptimasi dengan menggunakan Particle Swarm Optimization sehingga memperoleh akurasi sebesar 99.28% dan Algorittma C4.5 juga dioptimasi dengan menggunakan Genetic Algorithm sehingga memperoleh akurasi sebesar 99.38%.
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Fitria, Ainul, Salahuddin Salahuddin, and Muhammad Rizka. "Rancang Bangun Aplikasi Machine Learning Pemilihan Varietas Bibit Jagung Unggul Menggunakan Algoritma Artificial Neural Network (ANN) Berbasis Web." Journal of Artificial Intelligence and Software Engineering (J-AISE) 4, no. 1 (June 9, 2024): 27. http://dx.doi.org/10.30811/jaise.v4i1.5401.
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Jagung atau dalam bahasa latin Zea Mays merupakan adalah salah satu dari jenis tanaman pangan dari keluarga rumput-rumputan yang dikelompokkan dalam tanaman biji-bijian. Jagung memiliki banyak varietas. Adapun varietas yang telah dilepas oleh Menteri Pertanian hingga Oktober tahun 2022 sebanyak 361 varietas, yaitu jagung hibrida sebanyak 298 varietas, jagung komposit sebanyak 59 varietas, dan ada sebanyak 4 varietas jagung hibrida produk rekayasa genetik (PRG). Petani jagung biasanya memilih dan menentukan bibit jagung yang akan dibudidayakan berdasarkan rekomendasi pedagang bibit jagung atau dari rekan sesama petani jagung. Namun demikian sering dijumpai hasil panen jagung tidak sesuai dengan ekspektasi dan target yang diharapkan. Bahkan, tidak jarang petani jagung mengalami gagal panen yang disebabkan oleh beberapa faktor, salah satunya dikarenakan bibit jagung yang dipilih bukan merupakan varietas bibit jagung unggul. Sistem ini dirancang untuk membantu para petani jagung khususnya di daerah Aceh dalam memilih dan menentukan bibit jagung unggul untuk dibudidayakan dengan tujuan mendapatkan hasil panen yang memuaskan. Sistem ini menggunakan algoritma Artificial Neural Network untuk melakukan pemilihan. Artificial Neural Network (ANN) adalah algoritma Machine Learning dengan model komputasi yang terinspirasi dari prinsip kerja otak manusia. Artificial Neural Network digunakan dalam aplikasi ini karena dapat melakukan prediksi dengan akurat. Hasil yang diharapkan dengan adanya sistem ini petani dapat memilih varietas bibit jagung unggul untuk dibudidayakan, sehingga dapat memenuhi kebutuhan stok dalam negeri dengan memanfaatkan komputer dalam tahapan pemilihan bibit unggul. Penerapan algortima ANN Multi Layer Perceptron pada aplikasi ini menggunakan 21 data varietas jagung dengan 504 dataset yang dimasukkan mendapatkan hasil nilai tertinggi dengan persentase akurasi 90,47%. Dengan hasil tersebut, algortima Artificial Neural Network Multi Layer Perceptron dapat digunakan untuk Aplikasi Machine Learning dalam menentukan pemilihan varietas bibit jagung unggul Abstract Corn or in Latin Zea Mays is one of the types of food crops from the grass family which is grouped into grain crops. Corn has many varieties. The varieties that have been released by the Minister of Agriculture until October 2022 are 361 varieties, namely 298 varieties of hybrid corn, 59 varieties of composite corn, and there are as many as 4 varieties of genetically modified (PRG) hybrid corn. Maize farmers usually choose their maize seeds based on recommendations from maize seed traders or fellow maize farmers. However, maize yields are often not in line with expectations and targets. In fact, it is not uncommon for corn farmers to experience crop failure caused by several factors, one of which is because the corn seeds chosen are not superior corn seed varieties. This system is designed to help corn farmers, especially in the Aceh area, in choosing and determining superior corn seeds for cultivation with the aim of getting satisfactory yields. This system uses Artificial Neural Network algorithm to make the selection. Artificial Neural Network (ANN) is a Machine Learning algorithm with a computational model inspired by the working principles of the human brain. Artificial Neural Network is used in this application because it can make accurate predictions. The expected results with this system are that farmers can choose superior varieties of corn seeds to be cultivated, so that they can meet the needs of domestic stocks by utilizing computers in the stages of selecting superior seeds. The application of ANN Multi Layer Perceptron algortima in this application using 21 corn variety data with 504 datasets entered gets the highest value results with an accuracy percentage of 90.47%. With these results, the Artificial Neural Network Multi Layer Perceptron algortima can be used for Machine Learning applications in determining the selection of superior corn seed varieties.
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Franzen, Julian, Udo Pinders, and Bernd Kuhlenkötter. "Agent-Based Optimization of Maintenance Planning for Railway Vehicles." PHM Society European Conference 5, no. 1 (July 22, 2020): 9. http://dx.doi.org/10.36001/phme.2020.v5i1.1248.
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In practice, maintenance of rail vehicles is based on reactive and preventive maintenance strategies. Condition-oriented maintenance approaches are only slowly finding their way into the market. When researching the state of the art, it is noticeable that the majority of the approaches presented is considering individual components - the system focus necessary for maintenance optimization is not taken into account.
Depending on the target system (number of components) and planning period, a complex optimization problem (OP) results. The OP is an NP-heavy problem for which the use of Genetic Algortihms can deliver suitable solutions for small search spaces. When applying it on a complex system with a larger solution space, this heuristical approach alone is not sufficient for the analytical optimization of a system representing a locomotive.
Therefore, in this paper agent-based distributed problem solving is applied to analytically optimize the maintenance of the target system. Therefore, a multi-agent system (MAS) based on the O-MaSE-model will be developed, which captures the configuration of a target system and formulates the overall OP using the fictional data from a drivetrain of a shunting locomotive as an example. Following the principle of co-evolutionary problem solving, the overall problem is divided into smaller subproblems (SP). These SP have the right size to be solved by an own agent using genetic algorithms. In addition to, the solution focuses on the autonomous negotiation of an acceptable solution for the entire system by the SP agents.
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Indra, Zulfahmi, and Subanar Subanar. "Optimasi Biaya Distribusi Rantai Pasok Tiga Tingkat dengan Menggunakan Algoritma Genetika Adaptif dan Terdistribusi." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (July 31, 2014): 189. http://dx.doi.org/10.22146/ijccs.6546.
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AbstrakManajemen rantai pasok merupakan hal yang penting. Inti utama dari manajemen rantai pasok adalah proses distribusi. Salah satu permasalahan distribusi adalah strategi keputusan dalam menentukan pengalokasian banyaknya produk yang harus dipindahkan mulai dari tingkat manufaktur hingga ke tingkat pelanggan. Penelitian ini melakukan optimasi rantai pasok tiga tingkat mulai dari manufaktur-distributor-gosir-retail. Adapun pendekatan yang dilakukan adalah algoritma genetika adaptif dan terdistribusi. Solusi berupa alokasi banyaknya produk yang dikirim pada setiap tingkat akan dimodelkan sebagai sebuah kromosom. Parameter genetika seperti jumlah kromosom dalam populasi, probabilitas crossover dan probabilitas mutasi akan secara adaptif berubah sesuai dengan kondisi populasi pada generasi tersebut. Dalam penelitian ini digunakan 3 sub populasi yang bisa melakukan pertukaran individu setiap saat sesuai dengan probabilitas migrasi. Adapun hasil penelitian yang dilakukan 30 kali untuk setiap perpaduan nilai parameter genetika menunjukkan bahwa nilai biaya terendah yang didapatkan adalah 80,910, yang terjadi pada probabilitas crossover 0.4, probabilitas mutasi 0.1, probabilitas migrasi 0.1 dan migration rate 0.1. Hasil yang diperoleh lebih baik daripada metode stepping stone yang mendapatkan biaya sebesar 89,825. Kata kunci— manajemen rantai pasok, rantai pasok tiga tingkat, algortima genetika adaptif, algoritma genetika terdistribusi. Abstract Supply chain management is critical in business area. The main core of supply chain management is the process of distribution. One issue is the distribution of decision strategies in determining the allocation of the number of products that must be moved from the level of the manufacture to the customer level. This study take optimization of three levels distribution from manufacture-distributor-wholeshale-retailer. The approach taken is adaptive and distributed genetic algorithm. Solution in the form of allocation of the number of products delivered at each level will be modeled as a chromosome. Genetic parameters such as the number of chromosomes in the population, crossover probability and adaptive mutation probability will change adaptively according to conditions on the population of that generation. This study used 3 sub-populations that exchange individuals at any time in accordance with the probability of migration. The results of research conducted 30 times for each value of the parameter genetic fusion showed that the lowest cost value obtained is 80,910, which occurs at the crossover probability 0.4, mutation probability 0.1, the probability of migration 0.1 and migration rate 0.1. This result has shown that adaptive and distributed genetic algorithm is better than stepping stone method that obtained 89,825. Keywords— management supply chain, three level supply chain, adaptive genetic algorithm, distributed genetic algorithm.
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Ibnu Athaillah and M. Mujiono. "Optimasi Penempatan Tea Utensil dalam Nioh 2 Menggunakan Multiobjective NSGA-II." JAMI: Jurnal Ahli Muda Indonesia 3, no. 2 (April 14, 2023): 110–20. http://dx.doi.org/10.46510/jami.v3i2.133.
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Objektif. Dalam game komersial Nioh 2, terdapat sebuah mekanisme pemgembangan karakter yang bernama Tea Set. Mekanisme permainan ini menuntut pemain untuk mencari kombinasi empat Tea Utensil untuk memaksimalkan serangkaian bonus. Pada penelitian ini, percobaan dilakukan untuk mencari cara alternatif untuk memudahkan pemain mencari kombinasi yang tepat. Kriteria Tea Set yang baik tidak hanya ditandai dengan nilai-nilai bonus yang tinggi, tapi juga harus seimbang.
Material and Metode. Permasalahan ini dapat dikategorikan sebagai permasalahan multiobjective. Non-dominated Sorting Genetic Algorithm (NSGA-II) diusulkan sebagai algoritma untuk mencari kombinasi Tea Set untuk menghasilkan bonus yang kuat dan seimbang. Dalam game, sudah terdapat menu untuk mencari kombinasi tea set secara otomatis.
Tetapi tea set yang diberikan oleh menu tersebut terkadang memiliki kelemahan. Tea set yang dihasilkan oleh algoritma dibandingkan dengan hasil oleh menu tersebut.
Hasil. Ketika dibandingkan, tea set yang dihasilkan oleh algoritma cenderung lebih rendah pada nilai yang diprioritaskan. Akan tetapi nilai-nilai bonusnya lebih merata dan konsisten dibanding tea set dari menu yang terkadang mengabaikan nilai yang lain. Selain itu dalam nilai total, nailai bonus dari algoritma sering kali lebih tinggi.
Kesimpulan. Dalam kegiatan mengembangkan karakter, algortima ini dapat menjadi metode alternatif untuk mengoptimalkan kombinasi tea set. Implementasi dalam game juga dimungkinkan karena algoritma yang tergolong cepat ini tidak akan memberatkan komputasi.
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Grisales-Noreña, Luis Fernando, Oscar Danilo Montoya, and Alberto-Jesus Perea-Moreno. "Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions." Mathematics 11, no. 7 (March 26, 2023): 1604. http://dx.doi.org/10.3390/math11071604.
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This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time.
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Bimas Saputra, Ebit. "Perancangan Aplikasi Pengaturan Kapal Untuk Transportasi Semen Curah di PT Semen Padang Dengan Menggunakan Algoritma Genetika." Jurnal Siber Transportasi dan Logistik 1, no. 1 (April 1, 2023): 1–9. http://dx.doi.org/10.38035/jstl.v1i1.1.
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Abstract: Currently PT Semen Padang already has a ship scheduling plan for a certain period of time. However, this schedule may not be realized due to factors that cannot be controlled. In this study, a genetic algorithm method was used to help planners solve ship management problems for bulk cement distribution at PT Semen Padang. Researchers designed a more flexible application so that it can accommodate changes that occur in the distribution of bulk cement at PT Semen Padang. Based on these data, a trial scenario was carried out to find out the best parameters to solve the problem. The parameters tested were the number of generations with values 100, 200 and 300, population size with values 100, 150 and 200, the probability of crossing over with values of 60%, 65% and 70%, and the probability of mutation with values of 1%, 10 %, and 30%. The results of the implementation of the application resulted in a total logistics cost of IDR 13.79 billion.
Abstrak: Saat ini PT Semen Padang telah memiliki rencana penjadwalan kapal untuk jangka waktu tertentu. Namun penjadwalan ini bisa saja tidak direalisasikan karena adanya faktor-faktor yang tidak dapat dikendalikan. Pada penelitian ini digunakan metode algortima genetika untuk membantu perencana menyelesaikan permasalahan pengaturan kapal untuk distribusi semen curah di PT Semen Padang. Peneliti merancang aplikasi yang lebih fleksibel sehingga dapat mengakomodasikan perubahan yang terjadi pada pendistribusian semen curah di PT Semen Padang. Berdasarkan data tersebut dilakukan skenario uji coba untuk mengetahui parameter terbaik untuk menyelesaikan permasalahan. Parameter yang diuji coba yaitu banyak generasi dengan nilai 100, 200 dan 300, ukuran populasi dengan nilai 100, 150, dan 200, probabilitas pindah silang dengan nilai 60%, 65%, dan 70%, dan probabilitas mutasi dengan nilai 1%, 10%, dan 30%. Hasil dari implementasi dari aplikasi menghasilkan total biaya logistik yang diperoleh sebesar Rp 13,79 milyar.
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Harir, Zainul, Ida Bagus Ketut Widiartha, and Royana Afwani. "Aplikasi Pertimbangan Wisata di Pulau Lombok dengan Metode Fuzzy Mamdani & Algoritma Genetika." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 6 (December 2, 2020): 1261. http://dx.doi.org/10.25126/jtiik.2020721197.
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<p class="Abstrak">Pulau Lombok memiliki pariwisata berupa keindahan alam dan kebudayaan yang menarik, sehingga juga mendapat tiga penghargaan pada <em>World Halal Tourism Awards</em> 2016 dengan faktor pertumbuhan kunjungan wisatawan sebesar 13% pada tahun tersebut. Adanya sebuah aplikasi yang dapat membantu wisatawan dalam menentukan keputusan perjalanan wisata mereka adalah wajib. Aplikasi ini dikembangkan dengan logika <em>Fuzzy</em> Mamdani dan Algoritma Genetika dengan tujuan memberikan rekomendasi pariwisata.Logika <em>Fuzzy</em> Mamdani memberikan pertimbangan wisata berdasarkan 5 parameter (anggaran, rencana perjalanan, akomodasi, makanan dan minuman, serta biaya transportasi) yang kemudian menjadi 5 fungsi keanggotaan untuk membangun kombinasi aturan pada fuzzy dan menghasilkan keluaran berupa pertimbangan wisata, yaitu: Tidak Memungkinkan, Cukup Memungkinkan, dan Memungkinkan. Kombinasi lima fungsi keanggotaan tersebut, menghasilkan 10.080 aturan, yang digunakan untuk mengetahui seseorang memungkinkan, atau tidak untuk berwisata ke pulau Lombok dengan <em>constrain</em> parameter yang dimiliki, yang dibangkitkan dengan menggunakan fungsi Defuzzifikasi <em>Mean of Max</em> (MOM). Algortima Genetika digunakan dalam memberikan alokasi penggunaan budget yang optimal dalam berwisata di Pulau Lombok.Hasil pengujian dengan perhitungan manual dan model defuzzifikasi yang berbeda memiliki akurasi 100%. Untuk implementasi Algoritma Genetika, aplikasi memperoleh alokasi anggaran optimal pada <em>probabilitas crossover</em> (pc) dan probabilitas mutasi (pm) dengan (pc) 0,7 dan (pm) 0,2.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Tourism in Lombok has an interesting culture, it makes Lombok got three awards at the 2016 World Halal Tourism Awards and became a growth factor for tourist visits by 13% that year. An application that can help tourists in determining travel decision is mandatory.The application developed with Mamdani Fuzzy Logic and Genetic Algorithm to provide tourism recommendations. The Fuzzy Mamdani Logic Method provides tourism considerations based on 5 parameters (budget, travel plans, accommodation, food and drinks, and transportation costs) which then become 5 membership functions to build a combination of rules on fuzzy and produce output in the form of tourism's considerations: Not Enable, Enough Enable, and Enable. The combination of the 5 membership functions constructed 10.080 fuzzy rules, that's used to know wheater tourists enables them to go to Lombok with the limitation that they have. The defuzzification used is the Mean of Max (MOM). Genetic Algorithm (GA) is used in providing optimal budget allocation in traveling on Lombok IslandThe results of testing with manual calculations and different defuzzification models have 100% accurate, the application of GA obtained optimal budget allocation on crossover probability (pc) and mutation probability (pm) combination with (pc) 0.7 and (pm) 0.2.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>
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H. Al-Suhaili, Rafa, Rasul M. Khalaf, and Sanaa A.T. Al-Osmy. "A GENETIC ALGORITHM OPTIMIZATION MODEL FOR DIRECT PUMPING WATER SUPPLY INTAKES OPERATION." Malaysian Journal of Civil Engineering 26, no. 2 (July 2, 2018). http://dx.doi.org/10.11113/mjce.v26.15883.
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An optimization model for direct water supply intakes operation was created to maximize the rate of recovery of river bed, i.e. minimizing the effects pumping on river bed morphology. The model adopted constraints such as the water volume that should be pumped daily, minimum and maximum pumping intake capacity, and maximum time of pumping per day, while the objective function maximized is the rate of recovery of the river bed . The decision variables are selected as the pumping rate to river flow ratio, and the intake operation time to time of non-operation ratio. The model combines the Genetic Algorthim and the Artificial Neural Networks techniques to find the optimum solution. The application indicated that the required number of the randomly generated solutions and the number of cross over process for a stable optimum genetic algorithm solution are (100) and (1) respectively. Sensitivity analysis of the effect of number of cycles of operation and non-operation periods of the intake per day and the river flow indicates that these variables have no effect on the objective function.
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Anang Hidayat and Herdiesel Santoso*. "IMPLEMENTASI ALGORTIMA GENETIKA UNTUK OPTIMALISASI RUTE PENGIRIMAN PESANAN DI RESTO PAK LANJAR SLEMAN." PROSIDING SNAST, November 23, 2024, E68–77. https://doi.org/10.34151/prosidingsnast.v1i1.5080.
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Multi-destination travel is one of the problems in the Traveling Salesman Problem (TSP), which has a large problem space if solved combinatorially. This study aims to design and implement a Genetic Algorithm model to provide route recommendations for order deliveries at Resto Pak Lanjar, Sleman. The proposed Genetic Algorithm takes into account both symmetric and asymmetric distances. The route recommendations consider not only the distance but also the travel time, which is obtained using Google Maps API. The encoding scheme uses permutation encoding, parent selection is done through roulette-wheel selection, with order crossover as the crossover method and swap mutation as the mutation method. The algorithm also ensures that the best individual from a given generation is not lost during the evolutionary process. This study fills the gap in the literature, especially in applying Genetic Algorithms for route optimization in small restaurants by considering both time and distance factors. The experimental results show that for fewer than 8 objects, the optimal population size consists of 30 individuals, while for more than 8 objects, the optimal population size consists of 180 individuals. The stopping criterion is set when the highest fitness value remains unchanged for 30 consecutive generations. The optimal combination of crossover and mutation probabilities is {0.5:0.5}.
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"The Effect of Bat Algorithm and Genetic Algortihm on the Training Performance of Artificial Neural Networks." International Journal of Research Studies in Computer Science and Engineering 4, no. 4 (2017). http://dx.doi.org/10.20431/2349-4859.0404011.
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Kurniawan, Muchamad, and Nanik Suciati. "Modifikasi Kombinasi Particle Swarm Optimization dan Genetic Algorithm untuk Permasalahan Fungsi Non-Linier." INTEGER: Journal of Information Technology 2, no. 2 (September 29, 2017). http://dx.doi.org/10.31284/j.integer.2017.v2i2.177.
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Particle Swarm Optimization (PSO) is the population-based optimization algorithm and the generation of random values. The deficiency of the PSO algorithm is prematurely convergent, meaning it quickly finds solutions to local solutions. PSO tidak mampu untuk mencari ruang solusi lebih luas. PSO can not afford to search for wider solution space. In this study modification of the combination of PSO with Genetic Algortihm (GA) or we call M-PSOGA. The advantage of GA taken is to find a wider solution space. M-PSOGA is evaluated on non-linear function problem. The results obtained by M-PSOGA produce the best solution from its predecessor method, PSO and PSOGA. Better on the results of the solutions obtained and the convergent velocity on global solutions.Keywords: Particel Swarm Optimization, Genetic Algorithm, Non-Linier Function.
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Widayati, Ratna, Sholikhatun Sholikhatun, and Noorma Yulia Megawati. "Kendali Model Prediktif Kokoh pada Model Suhu Rumah Kaca." PYTHAGORAS Jurnal Pendidikan Matematika 17, no. 2 (December 14, 2022). http://dx.doi.org/10.21831/pythagoras.v17i2.51414.
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Dalam paper ini dibahas mengenai masalah sistem kendali suhu rumah kaca dengan mempertimbangkan variabel gangguan. Masalah kendali suhu rumah kaca ini dimodelkan dengan RMPC (Robust Model Predictive Control). Algoritma Particle Swarm Optimization (PSO) dan Genetic Algortihm (GA) digunakan untuk mencari penyelesaian masalah RMPC pada sistem suhu rumah kaca yang berupa masalah optimisasi berkendala. Berdasarkan hasil simulasi, teknik kendali RMPC mampu mengatur suhu rumah kaca sesuai suhu yang diinginkan dengan gangguan relatif kecil yaitu sebesar 0.09oC. Selain itu, waktu iterasi Algoritma PSO lebih cepat dalam menyelesaikan masalah RMPC pada sistem suhu rumah kaca dibandingkan dengan Algoritma Genetika.
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Garayev, Kenish, and Sedat Durmuşkaya. "Genetik Algortima ile Portföy Seçiminde Kriz Dönemi Etkisi, BİST-30’da Bir Uygulama." İşletme Bilimi Dergisi, December 31, 2017, 1–18. http://dx.doi.org/10.22139/jobs.328992.
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Muharni, Yusraini, Ade Irman Saeful M, and Tania Ero Rubyanti. "PENJADWALAN FLOW SHOP MESIN PARALEL MENGGUNAKAN METODE LONGEST PROCESSING TIME DAN CROSS ENTROPY-GENETIC ALGORITHM PADA PEMBUATAN PRODUK STEEL BRIDGE B-60." Jurnal Ilmiah Teknik Industri 7, no. 3 (January 16, 2020). http://dx.doi.org/10.24912/jitiuntar.v7i3.6338.
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Sistem produksi di sebuah perusahaan fabrikasi seingkali mengalami permasalahan salah satunya adalah ketika perusahaan tidak mampu mengirim order kepada customer di waktu yang telah ditentukan. Hal ini dapat menyebabkan perusahaan harus menanggung biaya pinalti. Masalah ini dapat diatasi apabila dilakukannya penjadwalan produksi dengan baik. Pentingnya penjadwalan produksi adalah untuk mengelola dengan tepat sumber daya yang ada agar terciptanya efektivitas dan efisiensi dalam penggunaan sumber daya. Salah satu usaha yang dilakukan untuk tercapainya penjadwalan yang optimal adalah dengan meminimalkan makespan. Produk Steel Bridge B-60 merupakan salah satu dari jenis jembatan Truss Bridge yang diproduksi oleh Perusahan Fabrikasi Baja Struktur Kontruksi yang berlokasi di Cilegon. Perusahan tersebut memiliki sistem produksi make to order dengan alur produksi flow shop dan susunan mesin paralel. Penelitian ini dilakukan dengan menerapkan pendekatan heuristic metode LPT (Longest Processing Time) dan pendekatan metaheuristic dengan menggunakan metode Cross Entropy yang dikombinasikan dengan Genetic Algorithm yang dikenal dengan sebutan CEGA (Cross Entropy Genetic Algortihm). Algoritma metaheuristic diterjemahkan ke dalam Bahasa pemograman Matlab. Hasil perbandingan kedua metode menunjukkan bahwa kedua metode usulan yaitu LPT dan CEGA lebih baik dari metode eksisting dan memiliki efisiensi sebesar 7.94%.
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SHANONO, Nura Jafar, Lawal AHMAD, Nuraddeen Mukhtar NASİDİ, Abdul'aziz Nuhu JİBRİL, and Mukhtar Nuhu YAHYA. "Simulation-Optimization Modelling of Yield and Yield Components of Tomato Crop." Turkish Journal of Agricultural Engineering Research, June 14, 2023. http://dx.doi.org/10.46592/turkager.1283793.
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This study simulate and optimize the yield and yield parameters of tomato using AquaCrop model and genetic algorthm (GA) respectively. The AquaCrop model was firstly calibrated using the data obtained from the field and was later used to simulate the observed yield, water productivity and biomass of tomato. The Root Mean Square Error (RMSE), Coefficient of Residual Mass (CRM) Normalized Root Mean Square Error (NRMSE) and Modelling efficiency (EF) were used to compare the observed and simulated values. The governing equation of AquaCrop simulation software was then optimized using the evolutionary optimization method of GA with MATLAB programming software. All the statistical indices except CRM used in comparing the simulated and observed values indicated good agreement. The CRM values of -0.11, -0.06 and -0.20 were obtained for the yield, biomass and water productivity of tomato which indicated a very slight over-estimation of the observed results by the AquaCrop model. The optimization algorithm terminated when the optimal values of yield and biomass were 4.496 〖ton ha〗^(-1) and 4.90 〖ton ha〗^(-1) respectively. The GA revealed that the yield and biomass of tomato can be increased by 57% and 23% respectively if the optimized parameters were either attained on the field experiment or used during simulation. Thus, the study ascertained that crop simulation models such as AquaCrop and optimization algorithms can be used to identify optimal parameters that if maintained on the field could improve the yield of crops such as tomato.
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Gsim, Jamal, Abdelilah Jalid, and Mohamed Zeriab Es-sadek. "Advanced hybrid algorithm for estimation of circularity deviation in coordinate metrology." Modelling and Simulation in Materials Science and Engineering, December 17, 2024. https://doi.org/10.1088/1361-651x/ada051.
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Abstract In the field of precision mechanics such as automobiles or aeronautics, the estimation of defects in the shape of mechanical parts is a very common task, and the declaration of conformity is mainly based on the precision of this value.
This research introduces an innovative hybrid optimization methodology that enhances the precision of estimation of circularity defects. The method proposed combins the Genetic Algorithm (GA) and the Interior Point Method (IPM), this approach overcomes the limitations of traditional methods like Least Squares (LS) and Orthogonal Distance Regression (ODR), which rely on initial estimates parameters to ensure convergence. The GA-IPM hybrid eliminates the need for such initial guesses, leading to faster convergence and more accurate results.
To control a mechanical part, a cloud of points is preleved by using a Coordinate Measuring Machines (CMMs), after tratement of those datasets the estimation of circularity defects is calculated using the proposed algortihm GA-IPM. To validate our approach a comparative study is carried out according to the standard ISO 10360-6, which consists of comparing the results (substitute geometry parameters) obtained with those of National Institute of Standards and Technology (NIST) considered as reference values. Several examples have been processed, circle totally or partially measured in order to verify the robustness of the algorithm in terms of convergence and calculation precision, the circularity defect is subsequently calculated on the basis of these parameters. The comparison shows that our method estimates the parameters and the circularity defect well without needing a vector of initial estimates parameters.
Given the results obtained, this hybrid approach represents a contribution to the estimation of the circularity deviation, thus offering industries a reliable, precise method to ensure the declaration of conformity of mechanical parts.