Multiobjective ranking binary artificial bee colony for gene selection problems using microarray datasets

2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiangtao Li ◽  
Mingjie Li ◽  
Minghao Yin
Keyword(s):  
Artificial Bee Colony ◽  
Gene Selection ◽  
Selection Problems ◽  
Bee Colony ◽  
Microarray Datasets

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.

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

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