An Improved Artificial Bee Colony Algorithm Based on Particle Swarm Optimization and Differential Evolution

In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

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Comparative Study on Synthesizing Reconfigurable Time- Modulated Linear Arrays using Differential Evolution, Artificial Bee Colony and Particle Swarm Optimization

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/67/1/012003 ◽

2014 ◽

Vol 67 ◽

pp. 012003 ◽

Cited By ~ 2

Author(s):

S K Mandal ◽

Harshavardhan Singh ◽

G K Mahanti ◽

Rowdra Ghatak

Keyword(s):

Particle Swarm Optimization ◽

Comparative Study ◽

Differential Evolution ◽

Artificial Bee Colony ◽

Particle Swarm ◽

Swarm Optimization ◽

Linear Arrays ◽

Bee Colony

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Evaluation performance study of Firefly algorithm, particle swarm optimization and artificial bee colony algorithm for non-linear mathematical optimization functions

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) ◽

10.1109/icccnt.2013.6726474 ◽

2013 ◽

Cited By ~ 6

Author(s):

Siddharth Agarwal ◽

Amrit Pal Singh ◽

Nitin Anand

Keyword(s):

Particle Swarm Optimization ◽

Artificial Bee Colony Algorithm ◽

Firefly Algorithm ◽

Artificial Bee Colony ◽

Particle Swarm ◽

Mathematical Optimization ◽

Performance Study ◽

Swarm Optimization ◽

Bee Colony ◽

Non Linear

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Performance Comparison of Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms for Fuzzy Modelling of Nonlinear Systems

Elektronika ir Elektrotechnika ◽

10.5755/j01.eie.22.5.16336 ◽

2016 ◽

Vol 22 (5) ◽

Cited By ~ 6

Author(s):

Mehmet Konar ◽

Aytekin Bagis

Keyword(s):

Particle Swarm Optimization ◽

Nonlinear Systems ◽

Differential Evolution ◽

Artificial Bee Colony ◽

Particle Swarm ◽

Performance Comparison ◽

Swarm Optimization ◽

Fuzzy Modelling ◽

Bee Colony

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Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing

The Journal of Supercomputing ◽

10.1007/s11227-018-2626-9 ◽

2018 ◽

Vol 75 (5) ◽

pp. 2455-2496 ◽

Cited By ~ 12

Author(s):

Jafar Meshkati ◽

Faramarz Safi-Esfahani

Keyword(s):

Cloud Computing ◽

Particle Swarm Optimization ◽

Resource Utilization ◽

Artificial Bee Colony ◽

Particle Swarm ◽

Energy Aware ◽

Swarm Optimization ◽

Bee Colony

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A Hybrid Classification Model Using Artificial Bee Colony with Particle Swarm Optimization and Minimum Relative Entropy as Post Classifier for Epilepsy Classification

Computational Vision and Bio Inspired Computing - Lecture Notes in Computational Vision and Biomechanics ◽

10.1007/978-3-319-71767-8_51 ◽

2018 ◽

pp. 593-603 ◽

Cited By ~ 2

Author(s):

Harikumar Rajaguru ◽

Sunil Kumar Prabhakar

Keyword(s):

Particle Swarm Optimization ◽

Relative Entropy ◽

Artificial Bee Colony ◽

Particle Swarm ◽

Classification Model ◽

Swarm Optimization ◽

Bee Colony ◽

Minimum Relative Entropy ◽

Hybrid Classification ◽

Epilepsy Classification

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

Jurnal Teknik Industri ◽

10.9744/jti.19.2.103-114 ◽

2018 ◽

Vol 19 (2) ◽

pp. 103 ◽

Cited By ~ 2

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.

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