Airfoil Topology Optimization using Teaching-Learning based Optimization

2015 ◽  
Vol 6 (1) ◽  
pp. 23-34
Author(s):  
Dushhyanth Rajaram ◽  
Himanshu Akhria ◽  
S. N. Omkar
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Spline Curves ◽  
Conceptual Design Phase ◽  
Design Variables ◽  
Heuristic Technique ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper primarily deals with the optimization of airfoil topology using teaching-learning based optimization, a recently proposed heuristic technique, investigating performance in comparison to Genetic Algorithm and Particle Swarm Optimization. Airfoil parametrization and co-ordinate manipulations are accomplished using piecewise b-spline curves using thickness and camber for constraining the design space. The aimed objective of the exercise was easy computation, and incorporation of the scheme into the conceptual design phase of a low-reynolds number UAV for the SAE Aerodesign Competition. The 2D aerodynamic analyses and optimization routine are accomplished using the Xfoil code and MATLAB respectively. The effects of changing the number of design variables is presented. Also, the investigation shows better performance in the case of Teaching-Learning based optimization and Particle swarm optimization in comparison to Genetic Algorithm.

scholarly journals Implementasi Metode Metaheuristik Symbiotic Organisms Search Dalam Penentuan Tata Letak Fasilitas Proyek Konstruksi Berdasarkan Jarak Tempuh Pekerja

2018 ◽  
Vol 19 (2) ◽  
pp. 103 ◽  
Author(s):  
Doddy Prayogo ◽  
Richard Antoni Gosno ◽  
Richard Evander ◽  
Sentosa Limanto
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Bee Colony ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Symbiotic Organisms

Penelitian ini menyelidiki performa dari metode metaheuristik baru bernama symbiotic organisms search (SOS) dalam menentukan tata letak fasilitas proyek konstruksi yang optimal berdasarkan jarak tempuh pekerja. Dua buah studi kasus tata letak fasilitas digunakan untuk menguji akurasi dan konsistensi dari SOS. Sebagai tambahan, tiga metode metaheuristik lainnya, yaitu particle swarm optimization, artificial bee colony, dan teaching–learning-based optimization, digunakan sebagai pembanding terhadap algoritma SOS. Hasil simulasi mengindikasikan bahwa algoritma SOS lebih unggul serta memiliki karakteristik untuk menghasilkan titik konvergen lebih cepat jika dibandingkan dengan metode metaheuristik lainnya dalam proses optimasi tata letak fasilitas proyek konstruksi.

Optimization Methods for Estimation of Missing Data

2010 ◽  
pp. 210-232
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Missing Data ◽  
Missing Values ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Hill Climbing ◽  
Swarm Optimization ◽  
Design Variables

This chapter presents various optimization methods to optimize the missing data error equation, which is made out of the autoassociative neural networks with missing values as design variables. The four optimization techniques that are used are: genetic algorithm, particle swarm optimization, hill climbing and simulated annealing. These optimization methods are tested on two datasets, namely, the beer taster dataset and the fault identification dataset. The results that are obtained are then compared. For these datasets, the results indicate that genetic algorithm approach produced the highest accuracy when compared to simulated annealing and particle swarm optimization. However, the results of these four optimization methods are the same order of magnitude while hill climbing produces the lowest accuracy.

Intelligence Optimization Design Model of Deep Cement-Stirring Pile

2010 ◽  
Vol 44-47 ◽  
pp. 1505-1508
Author(s):  
Xiang Yang Chen ◽  
Heng Zhen Yan
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Design Theory ◽  
Optimization Design ◽  
Particle Swarm ◽  
Composite Foundation ◽  
Swarm Optimization ◽  
Pile Diameter ◽  
Design Variables ◽  
Pile Length

Aiming at the phenomenon of the more conservative design of deep cement stirring pile currently, used optimization design theory such as genetic algorithm and particle swarm optimization, taken the cement consumption as the object function, taken replacement rate, water-cement ratio, pile diameter and pile length as the design variables, composite foundation bearing capacity and settlement as restrictive conditions, the optimal design models are established respectively based on genetic algorithm and particle swarm optimization. Case studies have shown that these two established models are effective. By comparison, the particle swarm optimization model is the more effective one.

Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment

2018 ◽  
Vol 17 (04) ◽  
pp. 1237-1267 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Hybrid Techniques ◽  
Heuristic Technique

Task scheduling is one of the most difficult problems which is associated with cloud computing. Due to its nature, as it belongs to nondeterministic polynomial time (NP)-hard class of problem. Various heuristic as well as meta-heuristic approaches have been used to find the optimal solution. Task scheduling basically deals with the allocation of the task to the most efficient machine for optimal utilization of the computing resources and results in better makespan. As per literature, various meta-heuristic algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and their other hybrid techniques have been applied. Through this paper, we are presenting a novel meta-heuristic technique — genetic algorithm enabled particle swarm optimization (PSOGA), a hybrid version of PSO and GA algorithm. PSOGA uses the diversification property of PSO and intensification property of the GA. The proposed algorithm shows its supremacy over other techniques which are taken into consideration by presenting less makespan time in majority of the cases which leads up to 22.2% improvement in performance of the system and also establishes that proposed PSOGA algorithm converges faster than the others.

scholarly journals OPTIMASI TOPOLOGI DAN UKURAN PENAMPANG STRUKTUR RANGKA BATANG BAJA DENGAN METODE METAHEURISTIK

2019 ◽  
Vol 6 (1) ◽  
pp. 33-42
Author(s):  
Ricky Agusta Hartono
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Symbiotic Organisms ◽  
Evolution Teaching

Optimasi topologi dari struktur rangka batang baja memberikan hasil yang lebih optimal dibandingkan optimasi ukuran penampang karena batang dan nodes yang tidak berguna pada struktur dapat dihilangkan. Fungsi objektif dari algoritma metaheuristik adalah untuk meminimalkan massa struktur rangka batang baja terhadap constraints statis dan dinamis berdasarkan studi kasus dan spesifikasi bangunan baja struktural Indonesia, SNI 1729:2015. Empat algoritma yang digunakan pada studi ini adalah: Particle Swarm Optimization, Differential Evolution, Teaching-Learning-Based Optimization, dan Symbiotic Organisms Search. Keempat algoritma tersebut diuji pada studi kasus 24-bar truss. Performa dari algoritma diukur dari lima kriteria massa, yaitu: massa terbaik, terburuk, rata-rata, standar deviasi, dan median dari struktur rangka batang baja. Hasil penelitian menunjukkan SOS menunjukkan performa terbaik pada studi kasus 24-bar truss.

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