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.

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.

The Binary Differential Search Algorithm Approach for Solving Uncapacitated Facility Location Problems

2017 ◽  
Vol 14 (1) ◽  
pp. 670-684 ◽  
Author(s):  
Mehmet Akif Sahman ◽  
Adem Alpaslan Altun ◽  
Abdullah Oktay Dündar
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Intelligence ◽  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Binary Optimization ◽  
Bee Colony

Recently, many Computational-Intelligence algorithms have been proposed for solving continuous problem. The Differential Search Algorithm (DSA), a computational-intelligence based algorithm inspired by the migration movements of superorganisms, is developed to solve continuous problems. However, DSA proposed for solving problems with continuous search space proposed for solving should be modified for solving binary structured problems. When the DSA is intended for use in binary problems, continuous variables need to be converted into binary format due to solution space structure of this type of problem. In this study, the DSA is modified to solve binary optimization problems by using a conversion approach from continuous values to binary values. The new algorithm has been designated as the binary DSA or BDSA for short. First, when finding donors with the BDSA, four search methods (Bijective, Surjective, Elitist1 and Elitist2) with different iteration numbers are used and tested on 15 UFLP benchmark problems. The Elitist2 approach, which provides the best solution of the four methods, is used in the BDSA, and the results are compared with Continuous Particle Swarm Optimization (CPSO), Continuous Artificial Bee Colony (ABCbin), Improved Binary Particle Swarm Optimization (IBPSO), Binary Artificial Bee Colony (binABC) and Discrete Artificial Bee Colony (DisABC) algorithms using UFLP benchmark problems. Results from the tests and comparisons show that the BDSA is fast, effective and robust for binary optimization.

scholarly journals Operation cost reduction in unit commitment problem using improved quantum binary PSO algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 1149 ◽  
Author(s):  
Ali Nasser Hussain ◽  
Ali Abduladheem Ismail
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Cost Reduction ◽  
Unit Commitment ◽  
Particle Swarm ◽  
Total Power ◽  
Binary Particle Swarm Optimization ◽  
Unit Commitment Problem ◽  
Swarm Optimization ◽  
Operation Cost

Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC is used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) 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.

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