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

2018 ◽  
Vol 75 (5) ◽  
pp. 2455-2496 ◽  
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

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.

Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization

2018 ◽  
pp. 337-363
Author(s):  
Prativa Agarwalla ◽  
Sumitra Mukhopadhyay
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Gene Selection ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
High Accuracy ◽  
Marker Genes ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Bee Colony

Pathway information for cancer detection helps to find co-regulated gene groups whose collective expression is strongly associated with cancer development. In this paper, a collaborative multi-swarm binary particle swarm optimization (MS-BPSO) based gene selection technique is proposed that outperforms to identify the pathway marker genes. We have compared our proposed method with various statistical and pathway based gene selection techniques for different popular cancer datasets as well as a detailed comparative study is illustrated using different meta-heuristic algorithms like binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE), binary coded artificial bee colony (BABC) and genetic algorithm (GA). Experimental results show that the proposed MS-BPSO based method performs significantly better and the improved multi swarm concept generates a good subset of pathway markers which provides more effective insight to the gene-disease association with high accuracy and reliability.

scholarly journals Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 603 ◽  
Author(s):  
Kuei-Hsiang Chao ◽  
Cheng-Chieh Hsieh
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Maximum ◽  
Photovoltaic Module ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Learning Factor

In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.

scholarly journals Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm

2020 ◽  
Author(s):  
Mohammed Ajuji ◽  
Aliyu Abubakar ◽  
Datti, Useni Emmanuel
Keyword(s):  
Particle Swarm Optimization ◽  
Time Complexity ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Worst Case ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Nature Inspired Algorithms

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

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