Learning parameter optimization of Multi-Layer Perceptron using Artificial Bee Colony, Genetic Algorithm and Particle Swarm Optimization

2015 ◽  
Author(s):  
Zehra Gulru Cam ◽  
Sibel Cimen ◽  
Tulay Yildirim
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Parameter Optimization ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Multi Layer Perceptron ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Learning Parameter

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 A fractional-order genetic algorithm for parameter optimization of the moisture movement in a bio-retention system

2019 ◽  
Vol 23 (4) ◽  
pp. 2343-2350
Author(s):  
Xiao-Hua Yang ◽  
Tong Liu ◽  
Yu-Qi Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Parameter Optimization ◽  
Particle Swarm ◽  
Absolute Error ◽  
Moisture Movement ◽  
Swarm Optimization ◽  
System A ◽  
Retention System

A bio-retention system is an important measure for non-point source pollution control. In order to improve the calculation precision for parameter optimization of the moisture movement in a bio-retention system, a real-encoded genetic algorithm based on the fractional-order operation is proposed, in which initial populations are generated by random mapping, and the searching range is automatically renewed with the excellent individuals by fractional-order particle swarm optimization operation. Its efficiency is verified experimentally. The results indicate that the absolute error by the fractional-order operation decreases by 67.73%, 62.23%, and 4.16%, and the relative error decreases by 42.88%, 35.76%, and 6.77%, respectively, compared to those by the standard binary-encoded genetic algorithm, random algorithm, and the particle swarm optimization algorithm. The fractional-order operation has higher precision and it is good for the practical parameter optimization in ecological environment systems.

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