Teaching-Learning-Based Artificial Bee Colony

2018 ◽  
pp. 166-178 ◽  
Author(s):  
Xu Chen ◽  
Bin Xu
Keyword(s):  
Artificial Bee Colony ◽  
Bee Colony ◽  
Teaching Learning

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.

scholarly journals Artificial Bee Colony Algorithm with Directed Scout

2021 ◽  
Author(s):  
Radhwan A.A. Saleh ◽  
Rüştü Akay
Keyword(s):  
Design Problem ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Real Life ◽  
Search Space ◽  
Bee Colony ◽  
Life Problems ◽  
Tension And Compression ◽  
Teaching Learning

Abstract As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.

Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms

2014 ◽  
Vol 48 ◽  
pp. 103-113 ◽  
Author(s):  
Ergun Uzlu ◽  
Murat İhsan Kömürcü ◽  
Murat Kankal ◽  
Tayfun Dede ◽  
Hasan Tahsin Öztürk
Keyword(s):  
Laboratory Tests ◽  
Artificial Bee Colony ◽  
Bee Colony ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation

2018 ◽  
Vol 212 ◽  
pp. 1578-1588 ◽  
Author(s):  
Xu Chen ◽  
Bin Xu ◽  
Congli Mei ◽  
Yuhan Ding ◽  
Kangji Li
Keyword(s):  
Parameter Estimation ◽  
Artificial Bee Colony ◽  
Solar Photovoltaic ◽  
Bee Colony ◽  
Photovoltaic Parameter ◽  
Teaching Learning
Sign in / Sign up

Export Citation Format

Share Document