scholarly journals Solving Economic Load Dispatch Problem using Particle Swarm Optimization and Artificial Bee Colony Optimization Algorithms

2018 ◽  
Vol 5 (7) ◽  
pp. 5-8
Author(s):  
N.D eexit ◽  
◽  
A.Surya Prakasa Rao
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

scholarly journals Optimal PMU Placement using Binary Particle Swarm and Artificial Bee Colony with Channel Limitations and Redundancy.

2019 ◽  
Vol 8 (3) ◽  
pp. 3881-3886
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Measurement Unit ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

Phasor Measurement Unit (PMU) being expensive and to be placed optimally, a meta-heuristic approach of Binary particle swarm optimization (BPSO) and Binary artificial bee colony optimization (BABC) is made for the optimal allocation of PMU in a power system. The PMU locations resulted are served by basic system conditions like network configuration, critical generators, and loads. The pattern of locations on including Zero-Injection Buess (ZIB) is also discussed. The redundancy in case of PMU loss is coined so as to obtain a complete observability of the power system. the channel limitations of device is also taken into consideration for better results in real-time systems. Optimal PMU locations for IEEE 30-bus and 14-bus systems with channel limits are compared with all above considerations. The number of PMU locations is reduced as channel limits increases. The simulated PMU locations are decreased with improved observability by Binary Artificial Bee Colony Optimization as compared to Binary Particle Swarm Optimization.

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.

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