Comparison of genetic algorithms and Particle Swarm Optimization (PSO) algorithms in course scheduling

Ramdania, Diena Rauda; Irfan, Mohamad; Alfarisi, Fariz; Nuraiman, Dian

doi:10.1088/1742-6596/1402/2/022079

Cited by 15 publications

(11 citation statements)

References 3 publications

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“…In this report, is more successful in evolving larger networks and the PSO is more successful on smaller networks. Ramdania et al [36] has reported that the PSO fitness value outperforms the genetic algorithm, but the genetic algorithm execution time is faster than the PSO algorithm. PSO involves less overall computation effort than GA but shown to outperform the GA for smaller population sizes [37].…”

Section: Resultsmentioning

confidence: 99%

Metaheuristic optimization in neural network model for seasonal data

2021

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The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data.

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Section: Resultsmentioning

confidence: 99%

Metaheuristic optimization in neural network model for seasonal data

2021

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“…Pada penelitian lainnya dilakukan perbandingan GA dan PSO. Hal ini sesuai pada makalah [25], nilai kesesuaian PSO mengungguli GA sehingga PSO memiliki kecenderungan nilai fitness lebih baik dari GA. Tetapi kecepatan eksekusi GA memiliki waktu yang lebih cepat dibandingkan PSO.…”

Section: Analisis Algoritmaunclassified

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Handayani¹,

Fudholi²,

Rani

2020

petir

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Penjadwalan mata kuliah merupakan hal penting yang dilakukan pada awal semester akademik. Proses penyusunan jadwal kuliah secara manual seringkali mengalami kesulitan karena terdapat beberapa konstrain sehingga membutuhkan waktu yang lama. Penelitian ini bertujuan mengkaji algoritma-algoritma yang sesuai dengan masalah penjadwalan mata kuliah. Pencarian dan analisis dilakukan terhadap literatur yang berkaitan dengan optimasi penjadwalan. Proses pencarian literatur dilakukan pada Google Scholar dan Science Direct dengan memasukkan kata kunci utama “course timetable”, “university timetable problem”, “school scheduling”, dan “algoritma penjadwalan”. Hasil analisis literatur meliputi sebaran domain, analisis algoritma serta gap dari penelitian sebelumnya. Pada penelitian sebelumnya terdapat kekurangan seperti algoritma yang tidak dapat menghasilkan solusi optimal. Hasil sebaran domain yang diperoleh ialah universitas dan sekolah dengan persentase 88% dan 12% dari keseluruhan makalah. Adanya temuan 14 sebaran algoritma dapat diklasifikasikan menjadi 3 metode, yaitu heuristic, metaheuristic, dan hyper-heuristic. Berdasarkan hasil analisis, dapat diberikan beberapa rekomendasi. Untuk optimasi yang cepat, Simulated Annealing (SA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.481-10.102s. Untuk solusi waktu dan nilai fitness terbaik, Genetic Algorithm (GA) dapat menjadi solusi karena mampu menghasilkan solusi dengan waktu 0.964-73.461s dan nilai fitness 1.

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“…) and lower limit ( ) of the design variable (Ramdania et al [23]), as shown in equations (1) and (2).…”

Section: Optimizationmentioning

confidence: 99%

“…By this initialization process, a swarm of particles can be distributed randomly to the design space. The position and velocity vectors are shown below [23]:…”

Section: Optimizationmentioning

confidence: 99%

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Multi-response Characteristics Optimization of a Paper-based Composite using Taguchi Method and Particle Swarm Optimization

Pangestu

Ayun

Wulandari

et al. 2020

jti

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Our dependency towards wood has reduced the number of forests especially in Indonesia. Hence, many people tried to use another material as a replacement of wood. One of them is composite. Department of Industrial Engineering Universitas Sebelas Maret has developed paper-based composite using newspaper waste in Praktikum Perancangan Teknik Industri (PPTI) II. Unfortunately, the quality of composite is not so good. This paper tried to understand the effect of factors to responses and discover the best combination of factors to obtain the best responses. The responses of this experiment are tensile strength, bending strength and impact value. The factors of this experiment are paper pulp, PVAc glue and water. Taguchi method is used to find the appropriate orthogonal array and Particle Swarm Optimization method to find the optimum combination of the factors. Orthogonal array used in this experiment is L9 (34). Through analysis, all of the factors are significant to tensile and bending strength, but they are not significant to impact value. The best combination of factors is 175 grams paper pulp, 130 grams PVAc glue and 175 ml water. Optimum results obtained for tensile strength is 0.7595 N/mm2, bending strength is 4.873 N/mm2 and impact value is 0.0138 J/mm2.

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Comparison of genetic algorithms and Particle Swarm Optimization (PSO) algorithms in course scheduling

Cited by 15 publications

References 3 publications

Metaheuristic optimization in neural network model for seasonal data

Metaheuristic optimization in neural network model for seasonal data

Kajian Algoritma Optimasi Penjadwalan Mata Kuliah

Multi-response Characteristics Optimization of a Paper-based Composite using Taguchi Method and Particle Swarm Optimization

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