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[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]© 2020, ICIC International. All rights reserved.Nature-inspired algorithms by mimicking nature strategies applicable to sort out hard optimization problems have received much attention by way of more efficient and comprehensive search mechanisms. The well-known artificial bee colony (ABC) is generated by imitating foraging behavior of honey bees for food sources. Many bee colonies exist in nature with different foraging behaviors as colony optimum mechanisms. This paper considers the stingless bee algorithm (SBA) to enrich swarm intelligence algorithm varieties from bee colonies, to explore the distinct foraging behaviors of stingless bee colony into search-based algorithm, and to know the capability of the SBA in numerical optimization problems. The developed SBA is applied in solving various numerical optimization problems results in high efficiency in acquiring a near-optimal solution. To acquire the algorithm performances, the proposed SBA is measured up with the ABC. The numerical results present SBA excels in average function evaluation to a solution (AES) and sum of errors (SE) criteria. In contrast, ABC exhibits better performances in success rate (SR) and final error (FE) criteria. The performance evaluations show bee colonies naturally develop optimal strategies in response to their environment and understanding of various foraging behaviors is practicable for constructing optimal bee colony algorithms.[/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]Artificial bee colonies (ABC),Bee colony algorithms,Nature inspired algorithms,Near-optimal solutions,Numerical optimizations,Optimization problems,Search-based algorithms,Swarm intelligence algorithms[/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]Artificial bee colony,Foraging behaviors,Numerical optimization,Stingless bee algorithm,Swarm intelligence[/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]Acknowledgment. This research was initially supported in part by the Ministry of Research, Technology and Higher Education, the Republic of Indonesia under the Decentralized Research Program on Excellent Research University, Institut Teknologi Bandung, Indonesia 2017 and was partially funded by the Research Program, Institut Teknologi Bandung 2019-2020.[/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/10.24507/ijicic.16.06.2063[/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]