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Using Particle Swarm and Brain Storm Optimization for Predicting Bus Arrival Time
Mores I.B.a, Fauzan M.a, Nazaruddin Y.Y.a,b, Ishaya Siregar P.a
a Institut Teknologi Bandung, Intrumentation Control Research Group, Bandung, Indonesia
b National Center for Sustainable Transportation Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Particle Swarm Optimization (PSO) and Brain Storm Optimization (BSO) are alternative methods to find out the optimized solution of a non-linear equation. This paper will discuss the application of both methods to find out the weight of neurons from Adaptive Neuro Fuzzy Inference Systems (ANFIS) technique, which is used in predicting the bus arrival time at the bus stop. Comparison of the performance from both methods will also be made. After the modeling, training and testing of the proposed algorithm, the RMSE value produced from ANFIS which was trained by the PSO testing was 0.8145, and if it was trained by BSO was 0.8352. These results also conclude that the ANFIS with PSO algorithm yields better predicting bus arrival time better rather than ANFIS BSO in this case.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Adaptive neuro-fuzzy inference system,Arrival time,Bus stop,Optimized solutions,Particle swarm,PSO algorithms,Training and testing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]adaptive neuro-fuzzy inference system,brain storm optimization,bus arrival time,particle swarm optimization.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This work was supported in part by USAID through the Sustainable Higher Education Research Alliances (SHERA) program under grant number IIE00000078-ITB-1.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEVT48285.2019.8993978[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]